Podcasts about clickhouse

Send us a textInvest in pre-IPO stocks with AG Dillon & Co. Contact aaron.dillon@agdillon.com to learn more. Financial advisors only. www.agdillon.com00:00 - Intro00:07 - ClickHouse $400M Series D $15B Valuation Langfuse M&A01:29 - Runpod $120M Run Rate 500K Developers 31 Regions02:59 - Humans& $480M Seed $4.48B Valuation 3 Months Old04:03 - Baseten $300M Raise $5B Valuation Nvidia $150M04:49 - Noveon Magnetics $215M Raise 2,000+ Tons Per Year Target06:17 - Zipline $600M+ New Funding $7.6B Valuation 2M+ Deliveries07:34 - Hatch Yelp Acquisition $300M Deal 12x ARR08:26 - Brex Capital One Acquisition $5.15B Deal $13B Deposits09:30 - DayOne Data Centers IPO Target Up to $20B10:14 - World Labs Funding Talks at $5B Potential $500M Balance Sheet Add11:08 - TikTok US New Entity 50% New Investors 200M US Users12:42 - Inferact $150M Seed $800M Valuation vLLM Commercialization13:16 - RadixArk $400M Valuation SGLang Commercial Push14:07 - Blue Origin TeraWave 6 Tbps 5,408 Satellites Q4 2027 Deployment15:15 - OpenAI Shopify Checkouts Jan 26 Launch 4% ChatGPT Fee16:24 - OpenAI API Revenue +$1B ARR in One Month $20B+ Total ARR16:51 - OpenAI Ads Testing Coming Weeks Go Plan Included17:31 - OpenAI ServiceNow 3-Year Enterprise Integration

AGENDA: 03:30 Can VC Survive With Public Market Prices Today 15:20 The Implosion of Thinking Machines 21:13 Elon Musk vs. OpenAI: The Legal Battle 40:50 Can OpenAI Win Ads? 55:50 ClickHouse's $15BN Deal: Analysed 58:55 Replit's $9BN Deal: Analysed 01:08:35 There Are Only Two Types of Deals VCs Want To Do Today      

This is a recap of the top 10 posts on Hacker News on January 17, 2026. This podcast was generated by wondercraft.ai (00:30): ASCII characters are not pixels: a deep dive into ASCII renderingOriginal post: https://news.ycombinator.com/item?id=46657122&utm_source=wondercraft_ai(01:55): The recurring dream of replacing developersOriginal post: https://news.ycombinator.com/item?id=46658345&utm_source=wondercraft_ai(03:20): If you put Apple icons in reverse it looks like someone getting good at designOriginal post: https://news.ycombinator.com/item?id=46663338&utm_source=wondercraft_ai(04:46): US electricity demand surged in 2025 – solar handled 61% of itOriginal post: https://news.ycombinator.com/item?id=46656903&utm_source=wondercraft_ai(06:11): PCs refuse to shut down after Microsoft patchOriginal post: https://news.ycombinator.com/item?id=46656998&utm_source=wondercraft_ai(07:37): Map To Poster – Create Art of your favourite cityOriginal post: https://news.ycombinator.com/item?id=46656834&utm_source=wondercraft_ai(09:02): ClickHouse acquires LangfuseOriginal post: https://news.ycombinator.com/item?id=46656552&utm_source=wondercraft_ai(10:28): After 25 years, Wikipedia has proved that news doesn't need to look like newsOriginal post: https://news.ycombinator.com/item?id=46656911&utm_source=wondercraft_ai(11:53): Raising money fucked me upOriginal post: https://news.ycombinator.com/item?id=46660543&utm_source=wondercraft_ai(13:19): 2025 was the third hottest year on recordOriginal post: https://news.ycombinator.com/item?id=46659913&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Jeremy Cohen est Principal Product Manager chez dbt, la solution de transformation souvent mentionnée sur le podcast. On en a parlé avec des 10aines de boîtes : BlaBlaCar, Doctolib mais aussi des plus grosses boîtes comme Decathlon.

What if your data platform could serve AI-native workloads while scaling reliably across your entire organization? In this episode, Benjamin sits down with Ritesh, Staff Engineer at Lyft, to explore how to build a unified data stack with Spark, Trino, and ClickHouse, why AI is reshaping infrastructure decisions, and the strategies powering one of the industry's most sophisticated data platforms. Whether you're architecting data systems at scale or integrating AI into your analytics workflow, this conversation delivers actionable insights into reliability, modernization, and the future of data engineering. Tune in to discover how Lyft is balancing open-source investments with cutting-edge AI capabilities to unlock better insights from data.

Data Science (click here to comment) 8. Dezember 2025, Jochen

Mardi 18 novembre, un peu après midi, Internet a toussé… puis s'est effondré par intermittence. En quelques minutes, Cloudflare, l'un des piliers de l'infrastructure du Web mondial, a entraîné dans sa chute une avalanche de services : ChatGPT, X/Twitter, Canva, Clubic et des milliers d'autres plateformes. L'hypothèse d'une cyberattaque massive a d'abord dominé. En réalité, la vérité est plus banale — et beaucoup plus inquiétante.Tout commence à 12h05, lorsque Cloudflare déploie une mise à jour sur un cluster de bases de données ClickHouse. Une modification censée renforcer la sécurité en rendant explicites les permissions d'accès. Un ajustement mineur, en apparence. Sauf que ce changement provoque un bug imprévu : chaque colonne de données se duplique dans les métadonnées. Une anomalie invisible pour l'utilisateur… mais catastrophique pour un composant clé : le fichier utilisé par le système Bot Management, chargé d'analyser le trafic pour distinguer humains et robots.Habituellement, ce fichier contient une soixantaine d'empreintes. Avec les doublons, il en compte plus de 200. Le problème ? Le logiciel censé le traiter est conçu pour refuser tout fichier dépassant 200 entrées, afin d'éviter une surcharge mémoire. Résultat : lorsque ce fichier corrompu se propage aux milliers de serveurs mondiaux, les machines plantent en série et renvoient des erreurs 500 aux internautes du monde entier. Le cauchemar se complique encore. Le fichier est régénéré toutes les cinq minutes. Selon que le serveur tombe sur une version saine ou défectueuse, Cloudflare oscille entre fonctionnement normal et blackout. Diagnostiquer la panne devient un casse-tête. Matthew Prince, le PDG, parle même d'une possible “démonstration de force” d'un réseau de bots, après les gigantesques attaques DDoS de juin.Ce n'est qu'à 14h04 qu'une piste interne apparaît. À 14h37, les équipes identifient enfin le coupable : le fichier Bot Management. À 15h24, sa génération automatique est stoppée. À 15h30, Internet redémarre. Enfin… presque. Le tableau de bord tombe à son tour, écrasé par le flot de connexions en attente. Il faudra attendre 18h06 pour un retour complet. Dans un mea culpa inhabituellement frontal, Matthew Prince avoue : « Une panne comme celle-ci est inacceptable. » Cloudflare promet des coupe-circuits plus rapides, une validation plus stricte des fichiers internes, et des limites pour ses outils de débogage, eux-mêmes responsables d'un ralentissement massif. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Christophe Blefari est le créateur de la newsletter data Blef.fr la plus connue en France. Il a été Head of Data, Head of Data Engineering et Staff Data Engineer dans des startups et des grands groupes et est selon moi l'un des plus grands experts data en France. Récemment, il a cofondé Nao Labs, un éditeur de code à destination des équipes Data qui utilisent l'IA.On décrypte les news data qu'il ne fallait pas rater en 2025.On aborde :

Episode #42 brings Tanya Bragin back to join Chad and Steve for the first sequel episode of the pod! Tanya's first episode was Episode 29 and it is the most watched episode so far.Tanya recently took on the VP of Marketing role at Clickhouse in addition to running Product so dive deep into her experience and how she's approaching Marketing differently from most companies since she's coming in as a technical product leader already. Lot of great insights here about how to do Demand Generation and Community Events differently from how companies typically run them.Episode #42 of "Can I get that software in blue?", a podcast by and for people engaged in technology sales. If you are in the technology presales, solution architecture, sales, support or professional services career paths then this show is for you!Our website: https://softwareinblue.comTwitter: https://twitter.com/softwareinblueLinkedIn: https://www.linkedin.com/showcase/softwareinblueMake sure to subscribe or follow us to get notified about our upcoming episodes:Youtube: https://www.youtube.com/channel/UC8qfPUKO_rPmtvuB4nV87rgApple Podcasts:https://podcasts.apple.com/us/podcast/can-i-get-that-software-in-blue/id1561899125Spotify: https://open.spotify.com/show/25r9ckggqIv6rGU8ca0WP2Links mentioned in the episode:Clickhouse HyperDX acquisition: https://clickhouse.com/blog/clickhouse-acquires-hyperdx-the-future-of-open-source-observabilityClickhouse PeerDB acquisition: https://clickhouse.com/blog/clickhouse-acquires-peerdb-to-boost-real-time-analytics-with-postgres-cdc-integrationDebugging story from Clickhouse SRE: https://clickhouse.com/blog/a-case-of-the-vanishing-cpu-a-linux-kernel-debugging-storyEssentialism Book: https://www.amazon.com/Essentialism-Disciplined-Pursuit-Greg-McKeown/dp/0804137382

In the episode, we sat down with ClickHouse Co-Founder Yury Izrailevsky to unpack how one of the fastest open-source databases in the world became the analytics engine of choice for 2,000 customers including Harvey, Canva, HP, and Supabase. From its Yandex origins to powering AI observability, Yury shares how ClickHouse balances open-source roots, cloud innovation, and a remote-first culture moving at breakneck speed.ClickHouse's Series C valued the company at $6.35B earlier this year, and just yesterday they announced an extension to that round, just months after it was raised. In this episode, we dig into:Origins & Founding StoryClickHouse began as an internal project at Yandex to power a Google Analytics–style platform, focused on performance and scale.Open-sourced in 2016 - rapid global adoption laid the foundation for ClickHouse the company. Yury first discovered ClickHouse while at Google; impressed by its speed, he later co-founded the company in 2021 alongside Aaron Katz (ex-Elastic) and the original creator Alexey Milovidov.Why ClickHouse Stands OutColumn-oriented, open source OLAP database designed for massive-scale analytical processing.Excels in performance, efficiency, and cost - ideal for large data volumes and real-time analytics (and now AI workloads). Architectural choices:Columnar storage = better compression and faster execution.Separation of compute and storage enables elasticity, scalability, and resilience in the cloud.Open Source vs. CloudOpen-source version offers freedom and flexibility.Cloud product delivers much lower total cost of ownership and fully managed experience.Architectural parity between the two ensuring no vendor lock-in for customers. Customers can run the same queries on both; most stay with cloud due to simplicity and cost efficiency.Use Cases & Ecosystem4 main use cases:Real-time analyticsData WarehousingObservability AI / ML WorkloadsCompany Building & CultureFully remote from day one.Prioritized experienced, self-sufficient engineers over early-career hires.Built and launched GA version in less than a year - insane pace of innovation.Innovation & CommunityMonthly release cadence.Hundreds of integrations and connectors.Strong open-source and commercial communityAdvice for FoundersFocus on what matters most Hire mature, independent thinkers.Move fast but maintain quality; ClickHouse Cloud achieved production-grade quality in record time.

Datenbanken sind das Rückgrat vieler Anwendungen, aber wie konsistent sind deine Daten eigentlich? Egal ob Banküberweisung, Sneaker-Kauf im Online-Shop oder das neueste Side-Project: Oft verbergen sich hinter der vermeintlich „sicheren“ Datenhaltung komplexe Stolperfallen. Wie funktionieren Transaktionen wirklich? Und warum kann ausgerechnet ein falsch gewähltes Isolationslevel zu Dirty Reads, non-repeatable Reads oder sogar zu Write Skew führen?Wir nehmen dich in dieser Episode mit auf eine Reise in die Tiefen der Konsistenzmodelle. Wolfi ist ehemaliger Forscher für Datenbanksysteme an der Uni Innsbruck. Mit ihm steigen wir ein in die Praxis und Theorie; Von Foreign Keys und Check Constraints bis hin zur Multi-Version Concurrency Control (MVCC). Du erfährst, was sich hinter Serializable, Repeatable Read, Read Committed und Read Uncommitted verbirgt und weshalb Tools wie Jepsen immer neue Fehler in selbst „sicheren“ Systemen aufdecken.Am Ende weißt du, warum dich auch als Entwickler:in das Thema Konsistenz, Isolationslevel und Transaktionsmanagement beschäftigen solltest.Bonus: Dirty Reads sind wie Gerüchte: Man hört sie, bevor sie wahr sind… aber was, wenn sie nie stimmen?Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Would you like access to our advanced agency training for FREE? https://www.agencymastery360.com/training Are you still thinking of AI as just “ChatGPT with a better prompt”? Or maybe you've played around with Zapier automations and thought, yeah, that's good enough. Today's featured guest knows that the agencies pulling ahead right now are building full-on AI agent networks that replace routine tasks, streamline data pipelines, and give their teams superpowers. She's re-engineering her agency around AI and will talk about where she finds top-tier talent and why you don't need to code to lead your agency into the future. Jennifer Bagley is the CEO and founder of CI Web Group, a fully virtual digital marketing agency registered in 22 U.S. states with clients across the United States and Canada. A former corporate operator turned entrepreneur, Jennifer started in real estate and mortgage brokerage before leaning into the marketing work she built to support those businesses. Today she runs a modern, tech-forward agency that's rebuilt its stack around AI, centralized data, and agentic networks, all while carrying the scars and lessons of scaling, pivoting, and re-founding a business from the ground up. In this episode, we'll discuss: Feeling trapped by the business. Hiring, firing, and the people reset AI, reskilling, and the end of “middle” roles What does this talent cost? Subscribe Apple | Spotify | iHeart Radio Sponsors and Resources E2M Solutions: Today's episode of the Smart Agency Masterclass is sponsored by E2M Solutions, a web design, and development agency that has provided white-label services for the past 10 years to agencies all over the world. Check out e2msolutions.com/smartagency and get 10% off for the first three months of service. From Corporate Ladder to Accidental Agency Founder Jennifer came from an operations background, a self-proclaimed black belt in Six Sigma and certified project manager. Having built that corporate background, she had made a promise to herself (“by 30 I'll be an entrepreneur”), and started to build the side hustle that became the main event. She started in real estate and mortgage brokering where she had to learn marketing the hard way; not because she wanted to be a marketer, but because the survival of her businesses depended on it. Initially, Jennifer didn't set out to build a scalable agency; she built a team to support her broker network. When the market collapsed in 2008, the same team that did marketing for agents suddenly had a market outside real estate. That “we'll just help this painter or HVAC company” phase is where the web group was born: small, service-focused, and useful to people in her network. That accidental turn became a business by solving real, pressing problems for paying clients, then leaned into that. Trading Time for Freedom: The Hard Pivot For the first five years, Jennifer describes the business as a “lifestyle” operation, profitable maybe, but trapping her time. She was trading billable hours for income and was reaching her limit when she hired a coach that forced a reckoning: if entrepreneurship isn't buying you time, money, and freedom, what's the point? So she made the brutal choice of cutting consulting contracts and burning the bridge to the “safety” of hourly work, and effectively gave herself a mulligan. This is the classic founder pivot: you have to choose between growth that keeps you doing the work and growth that scales the business without you. Jennifer's reset wasn't pretty, for a while she lost everything and she and her son lived in an office for a while, but it bought her the permission to build something salable, not just sustainable. Agency owners who feel trapped in delivery need to remember that sometimes you have to give up short-term revenue to create long-term value. Feeling Trapped by the Agency and Becoming a CEO Those first five years, Jennifer continued to run a business that started as a supply chain consulting and eventually turned into a sales supply chain consulting. This change meant the business was now a good lead generator for the agency but it also meant Jennifer was essentially selling her image and her time. Until she ran out of time. Once she felt trapped by the business, Jennifer actually hired a business coach that helped her change the model from “selling Jennifer with marketing on the side” to an actual sustainable business. She had to go back to the basics and remember she, like every entrepreneur, started the business with the idea of having more time, money, and freedom. It took losing everything, but Jennifer knew she didn't want a lifestyle business, she wanted a sellable business. The antidote was delegation plus systems. If you want growth and a future exit, you need to own those CEO responsibilities and be comfortable with letting go of the day-to-day. Hiring, Firing, and Resetting the Team Jennifer's talent strategy has evolved with each stage of growth. Her early hires were the classic “friends, family, fools” bootstrap crew; later she invested in developers, content teams, project managers, and over time, more strategic hires like CFOs, chief of staff, BI teams, and AI engineers. Each five-year arc brought a new set of needs and a new level of sophistication in hiring. Now, she divides her time between promoting her agency's work in podcasts and content and thinking of ways to navigate her business in these volatile and exciting times. Her most recent addition to the team was a technology and transformation team that is revisiting all of the agency's processes, investments, and infrastructure. As a result, she has downsized her team from over 300 W2 employees and refocus the team. The takeaway for agency owners: be honest about whether your people are builders or maintainers, and hire accordingly. The workforce you need for growth is not the same as the workforce you need for stable operations. Building AI Agent Networks with Centralized Data Jennifer's agency shifted from WordPress to Webflow and built agentic networks: hundreds of AI agents that crawl competitors, do strategy homework, and automate tasks that humans used to do. More importantly, they rebuilt infrastructure into a hub-and-spoke model with a centralized min.io data layer and ETL pipelines feeding analytics and BI. Two big lessons here. One: invest in your tech stack deliberately so you're not a Frankenstein of five different platforms that don't talk to each other. Two: design your data architecture so your people (and your AI agents) have a single source of truth. That's how you get from fire-fighting in six dashboards to proactive, predictive signals that tell you when a client engagement needs attention. AI, Reskilling, and Shrinking Middle Roles Jennifer draws a hard line: the agency now tends to hire either very seasoned client-facing leaders or AI engineers; the middle is shrinking. With agentic networks giving junior staff “superpowers,” the agency can afford fewer mid-level “lever pullers.” At this level there's no room for slow execution or elementary work. That's a cultural and ethical challenge, both for hiring and for workforce development. For agency owners, this raises practical HR questions: do you reskill your people, or replace them? Jennifer suggests building agent-driven systems that augment humans, and being brutally honest about who can grow into that future. It's also a call to action for how we prepare the next generation: schools won't teach this; companies will need to. Playing with AI Platforms: Why Leaders Need to Just Know Enough to Be Dangerous Jennifer started like a lot of agency owners dipping into AI, playing around on tools like n8n, Make.com, Relevance, and Longchain. Her dev team laughed, calling her an “elementary school kid on a tricycle,” but here's the point: she didn't need to master the tech. She needed to know enough to point her team in the right direction. Instead of obsessing over code, she framed the problem differently: “Here's what I don't want a human doing anymore. Can you make that happen?” That mindset shift is key for agency owners. You don't need to be a full-stack AI engineer to lead an agency into the future; you just need to clearly define outcomes and invest in people who can deliver them. Find Real AI Talent in Unlikely Places This is where most agencies get stuck. You're not going to find your next AI architect on Upwork. Jennifer leaned on her network, starting with her cousin Chris, a hardcore developer who initially thought AI platforms were “rookie business.” Once Chris realized the power of agentic networks to scale his expertise, he became the backbone of CI Web Group's transformation. Now, she hunts talent in unconventional places: hackathons, LinkedIn, and especially YouTube. Forget the flashy “10x growth hack” videos — she looks for nerds with four views, geeking out about orchestrators and ETL pipelines. Those are the builders who care about solving real problems, not just building hype. Her tip: if you find one, reach out immediately. They don't want sales, they just want to build. Designing AI Agents Like an Agency Org Chart Jennifer compares AI agents to a company org chart. You don't hire one person to do everything, that's a recipe for burnout. Same thing with AI. Each agent should tightly focus on a single task, with checks, auditors, and orchestrators overseeing the system. The payoff was massive efficiency gains. Instead of six different platforms that don't talk, her agency built a centralized hub with min.io, ClickHouse, and AI layers on top. That's how you go from patchwork automation to true predictive intelligence. The Real Cost of AI Talent If you're wondering how much this all costs, the answer is… a lot. On the high end, seasoned AI engineers can run you a quarter million in salary. On the low end, Jennifer tests new hires on project-based sprints, maybe $6K for a 10-hour challenge. The point isn't to cut costs; it's to prove quickly who can deliver and who can't. Her recruiting process is brutal but effective: give candidates a project, a tight deadline, and see how they perform. If they stall, they're out. If they screen-share fast and solve problems live, they're in. No fluff, no endless interviews. Do You Want to Transform Your Agency from a Liability to an Asset? Looking to dig deeper into your agency's potential? Check out our Agency Blueprint. Designed for agency owners like you, our Agency Blueprint helps you uncover growth opportunities, tackle obstacles, and craft a customized blueprint for your agency's success.

Wie würdest du ... Open Podcasts … bauen? Architektur- und Design-Diskussion, die zweite.Monolith oder Microservices? Python oder Go? Wer träumt nachts eigentlich vom perfekten ETL-Stack? Als Softwareentwickler:in kennst du das: Daten aus zig Quellen, kapriziöse APIs, Security-Bedenken und der Wunsch nach einem skalierbaren, sauberen Architekturkonzept. Fragen über Fragen und etliche mögliche Wege. Welcher ist “der Richtige”?Genau dieses Szenario nehmen wir uns zur Brust: Wolfi hat mit „Open Podcast“ ein reales Projekt gebaut, das Analytics-Daten aus Plattformen wie Spotify, Apple & Co. zusammenführt. Du willst wissen, wie du verteilte APIs knackst, Daten harmonisierst, Backups sicherst und deine Credentials nicht als Excel-Sheet auf den Desktop legst? Komm mit auf unseren Architektur-Deepdive! Andy wird Schritt für Schritt interviewt und challenged, wie er als Engineer, von API-Strategie über Message Queues bis Security und Skalierung, dieses Problem kreativ lösen würde. Nebenbei erfährst du alles Wichtige über Open-Source-Vorteile, Datenbanken (PostgreSQL, Clickhouse), Backups, Monitoring und DevOps. Das Ganze immer garniert mit Learnings aus der echten Praxis.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

At Berlin Buzzwords, industry voices highlighted how search is evolving with AI and LLMs.- Kacper Łukawski (Qdrant) stressed hybrid search (semantic + keyword) as core for RAG systems and promoted efficient embedding models for smaller-scale use.- Manish Gill (ClickHouse) discussed auto-scaling OLAP databases on Kubernetes, combining infrastructure and database knowledge.- André Charton (Kleinanzeigen) reflected on scaling search for millions of classifieds, moving from Solr/Elasticsearch toward vector search, while returning to a hands-on technical role.- Filip Makraduli (Superlinked) introduced a vector-first framework that fuses multiple encoders into one representation for nuanced e-commerce and recommendation search.- Brian Goldin (Voyager Search) emphasized spatial context in retrieval, combining geospatial data with AI enrichment to add the “where” to search.- Atita Arora (Voyager Search) highlighted geospatial AI models, the renewed importance of retrieval in RAG, and the cautious but promising rise of AI agents.Together, their perspectives show a common thread: search is regaining center stage in AI—scaling, hybridization, multimodality, and domain-specific enrichment are shaping the next generation of retrieval systems.Kacper Łukawski Senior Developer Advocate at Qdrant, he educates users on vector and hybrid search. He highlighted Qdrant's support for dense and sparse vectors, the role of search with LLMs, and his interest in cost-effective models like static embeddings for smaller companies and edge apps. Connect: https://www.linkedin.com/in/kacperlukawski/Manish Gill Engineering Manager at ClickHouse, he spoke about running ClickHouse on Kubernetes, tackling auto-scaling and stateful sets. His team focuses on making ClickHouse scale automatically in the cloud. He credited its speed to careful engineering and reflected on the shift from IC to manager. Connect: https://www.linkedin.com/in/manishgill/André Charton Head of Search at Kleinanzeigen, he discussed shaping the company's search tech—moving from Solr to Elasticsearch and now vector search with Vespa. Kleinanzeigen handles 60M items, 1M new listings daily, and 50k requests/sec. André explained his career shift back to hands-on engineering. Connect: https://www.linkedin.com/in/andrecharton/Filip Makraduli Founding ML DevRel engineer at Superlinked, an open-source framework for AI search and recommendations. Its vector-first approach fuses multiple encoders (text, images, structured fields) into composite vectors for single-shot retrieval. His Berlin Buzzwords demo showed e-commerce search with natural-language queries and filters. Connect: https://www.linkedin.com/in/filipmakraduli/Brian Goldin Founder and CEO of Voyager Search, which began with geospatial search and expanded into documents and metadata enrichment. Voyager indexes spatial data and enriches pipelines with NLP, OCR, and AI models to detect entities like oil spills or windmills. He stressed adding spatial context (“the where”) as critical for search and highlighted Voyager's 12 years of enterprise experience. Connect: https://www.linkedin.com/in/brian-goldin-04170a1/Atita Arora Director of AI at Voyager Search, with nearly 20 years in retrieval systems, now focused on geospatial AI for Earth observation data. At Berlin Buzzwords she hosted sessions, attended talks on Lucene, GPUs, and Solr, and emphasized retrieval quality in RAG systems. She is cautiously optimistic about AI agents and values the event as both learning hub and professional reunion. Connect: https://www.linkedin.com/in/atitaarora/

Nik and Michael discuss when not to use Postgres — specifically use cases where it still makes sense to store data in another system. Here are some links to things they mentioned:Just use Postgres (blog post by Ethan McCue) https://mccue.dev/pages/8-16-24-just-use-postgresJust Use Postgres for Everything (blog post by Stephan Schmidt) https://www.amazingcto.com/postgres-for-everythingReal-time analytics episode https://postgres.fm/episodes/real-time-analyticsCrunchy Data Joins Snowflake https://www.crunchydata.com/blog/crunchy-data-joins-snowflakeTwo sizes fit most: PostgreSQL and Clickhouse (blog post by Sid Sijbrandij) https://about.gitlab.com/blog/two-sizes-fit-most-postgresql-and-clickhousepg_duckdb episode https://postgres.fm/episodes/pg_duckdbCloudberry https://github.com/apache/cloudberryTime-series considerations episode https://postgres.fm/episodes/time-series-considerationsQueues in Postgres episode https://postgres.fm/episodes/queues-in-postgresLarge Objects https://www.postgresql.org/docs/current/largeobjects.html PGlite https://pglite.devParadeDB https://www.paradedb.comZomboDB https://github.com/zombodb/zombodbturbopuffer https://turbopuffer.comHNSW vs. DiskANN (blog post by Haziqa Sajid) https://www.tigerdata.com/learn/hnsw-vs-diskannSPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search (paper) https://www.microsoft.com/en-us/research/wp-content/uploads/2021/11/SPANN_finalversion1.pdfAmazon S3 Vectors https://aws.amazon.com/s3/features/vectorsIterative Index Scans added to pgvector in 0.8.0 https://github.com/pgvector/pgvector/issues/678S3 FDW from Supabase https://github.com/supabase/wrappers/tree/main/wrappers/src/fdw/s3_fdw~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

The ClickHouse open source project has gained interest in the observability community, thanks to its outstanding performance benchmarks. Now ClickHouse is doubling down on observability with the release of ClickStack, a new open source observability stack that bundles in ClickHouse, OpenTelemetry and  HyperDX frontend. I invited Mike Shi, the co-founder of HyperDX and co-creator of ClickStack, to tell us all about this new project. Mike is Head of Observability at ClickHouse, and brings prior observability experience with Elasticsearch and more.You can read the recap post: https://medium.com/p/73f129a179a3/Show Notes:00:00 episode and guest intro04:38 taking the open source path as an entrepreneur10:51 the HyperDX observability user experience 16:08 challenges in implementing observability directly on ClickHouse20:03 intro to ClickStack and incorporating OpenTelemetry32:35 balancing simplicity and flexibility36:15 SQL vs. Lucene query languages 39:06 performance, cardinality and the new JSON type52:14 use cases in production by OpenAI, Anthropic, Tesla and more55:38 episode outroResources:HyperDX https://github.com/hyperdxio/hyperdx ClickStack https://clickhouse.com/docs/use-cases/observability/clickstack Shopify's Journey to Planet-Scale Observability: https://medium.com/p/9c0b299a04ddClickHouse: Breaking the Speed Limit for Observability and Analytics https://medium.com/p/2004160b2f5e New JSON data type for ClickHouse: https://clickhouse.com/blog/a-new-powerful-json-data-type-for-clickhouseSocials:BlueSky: https://bsky.app/profile/openobservability.bsky.socialTwitter: ⁠https://twitter.com/OpenObserv⁠LinkedIn: https://www.linkedin.com/company/openobservability/YouTube: ⁠https://www.youtube.com/@openobservabilitytalks⁠Dotan Horovits============Twitter: @horovitsLinkedIn: www.linkedin.com/in/horovitsMastodon: @horovits@fosstodonBlueSky: @horovits.bsky.socialMike Shi=======Twitter: https://x.com/MikeShi42LinkedIn: https://www.linkedin.com/in/mikeshi42BlueSky: https://bsky.app/profile/mikeshi42.bsky.socialOpenObservability Talks episodes are released monthly, on the last Thursday of each month and are available for listening on your favorite podcast app and on YouTube.

AI is driving a gold rush in compute power. And Nebius is quietly selling the picks and shovels.In today's episode, Finimize Analyst Russell Burns joins the pod to walk us through why this under-the-radar AI infrastructure firm might be one of the most overlooked growth stories out there. It's lean, it's scaling fast, it's tight with Nvidia - and its stake in ClickHouse alone could be worth a chunk of its market cap. Try Finimize Pro

What's up everyone, today we have the pleasure of sitting down with István Mészáros, Founder and CEO of Mitzu.io. (00:00) - Intro (01:00) - In This Episode (03:39) - How Warehouse Native Analytics Works (06:54) - BI vs Analytics vs Measurement vs Attribution (09:26) - Merging Web and Product Analytics With a Zero-Copy Architecture (14:53) - Feature or New Category? What Warehouse Native Really Means For Marketers (23:23) - How Decoupling Storage and Compute Lowers Analytics Costs (29:11) - How Composable CDPs Work with Lean Data Teams (34:32) - How Seat-Based Pricing Works in Warehouse Native Analytics (40:00) - What a Data Warehouse Does That Your CRM Never Will (42:12) - How AI-Assisted SQL Generation Works Without Breaking Trust (50:55) - How Warehouse Native Analytics Works (52:58) - How To Navigate Founder Burnout While Raising Kids Summary: István built a warehouse-native analytics layer that lets teams define metrics once, query them directly, and skip the messy syncs across five tools trying to guess what “active user” means. Instead of fighting over numbers, teams walk through SQL together, clean up logic, and move faster. One customer dropped their bill from $500K to $1K just by switching to seat-based pricing. István shares how AI helps, but only if you still understand the data underneath. This conversation shows what happens when marketing, product, and data finally work off the same source without second-guessing every report.About IstvánIstvan is the Founder and CEO of Mitzu.io, a warehouse-native product analytics platform built for modern data stacks like Snowflake, Databricks, BigQuery, Redshift, Athena, Postgres, Clickhouse, and Trino. Before launching Mitzu.io in 2023, he spent over a decade leading high-scale data engineering efforts at companies like Shapr3D and Skyscanner. At Shapr3D, he defined the long-term data strategy and built self-serve analytics infrastructure. At Skyscanner, he progressed from building backend systems serving millions of users to leading data engineering and analytics teams. Earlier in his career, he developed real-time diagnostic and control systems for the Large Hadron Collider at CERN. How Warehouse Native Analytics WorksMarketing tools like Mixpanel, Amplitude, and GA4 create their own versions of your customer. Each one captures data slightly differently, labels users in its own format, and forces you to guess how their identity stitching works. The warehouse-native model removes this overhead by putting all customer data into a central location before anything else happens. That means your data warehouse becomes the only source of truth, not just another system to reconcile.István explained the difference in blunt terms. “The data you're using is owned by you,” he said. That includes behavioral events, transactional logs, support tickets, email interactions, and product usage data. When everything lands in one place first (BigQuery, Redshift, Snowflake, Databricks) you get to define the logic. No more retrofitting vendor tools to work with messy exports or waiting for their UI to catch up with your question.In smaller teams, especially B2C startups, the benefits hit early. Without a shared warehouse, you get five tools trying to guess what an active user means. With a warehouse-native setup, you define that metric once and reuse it everywhere. You can query it in SQL, schedule your campaigns off it, and sync it with downstream tools like Customer.io or Braze. That way you can work faster, align across functions, and stop arguing about whose numbers are right.“You do most of the work in the warehouse for all the things you want to do in marketing,” István said. “That includes measurement, attribution, segmentation, everything starts from that central point.”Centralizing your stack also changes how your data team operates. Instead of reacting to reporting issues or chasing down inconsistent UTM strings, they build shared models the whole org can trust. Marketing ops gets reliable metrics, product teams get context, and leadership gets reports that actually match what customers are doing. Nobody wins when your attribution logic lives in a fragile dashboard that breaks every other week.Key takeaway: Warehouse native analytics gives you full control over customer data by letting you define core metrics once in your warehouse and reuse them everywhere else. That way you can avoid double-counting, reduce tool drift, and build a stable foundation that aligns marketing, product, and data teams. Store first, define once, activate wherever you want.BI vs Analytics vs Measurement vs AttributionBusiness intelligence means static dashboards. Not flexible. Not exploratory. Just there, like laminated truth. István described it as the place where the data expert's word becomes law. The dashboards are already built, the metrics are already defined, and any changes require a help ticket. BI exists to make sure everyone sees the same numbers, even if nobody knows exactly how they were calculated.Analytics lives one level below that, and it behaves very differently. It is messy, curious, and closer to the raw data. Analytics splits into two tracks: the version done by data professionals who build robust models with SQL and dbt, and the version done by non-technical teams poking around in self-serve tools. Those non-technical users rarely want to define warehouse logic from scratch. They want fast answers from big datasets without calling in reinforcements.“We used to call what we did self-service BI, because the word analytics didn't resonate,” István said. “But everyone was using it for product and marketing analytics. So we changed the copy.”The difference between analytics and BI has nothing to do with what the tool looks like. It has everything to do with who gets to use it and how. If only one person controls the dashboard, that is BI. If your whole team can dig into campaign performance, break down cohorts, and explore feature usage trends without waiting for data engineering, that is analytics. Attribution, ML, and forecasting live on top of both layers. They depend on the raw data underneath, and they are only useful if the definitions below them hold up.Language often lags behind how tools are actually used. István saw this firsthand. The product stayed the same, but the positioning changed. People used Mitzu for product analytics and marketing performance, so that became the headline. Not because it was a trend, but because that is what users were doing anyway.Key takeaway: BI centralizes truth through fixed dashboards, while analytics creates motion by giving more people access to raw data. When teams treat BI as the source of agreement and analytics as the source of discovery, they stop fighting over metrics and start asking better questions. That way you can maintain trusted dashboards for executive reporting and still empower teams to explore data without filing tickets or waiting days for answers.Merging Web and Product Analytics With a Zero-Copy ArchitectureMost teams trying to replace GA4 end up layering more tools onto the same mess. They drop in Amplitude or Mixpanel for product analytics, keep something else for marketing attribution, and sync everything into a CDP that now needs babysitting. Eventually, they start building one-off pipelines just to feed the same events into six different systems, all chasing slightly different answers to the same question.István sees this fragmentation as a byproduct of treating product and marketing analytics as separate functions. In categorie...

Alan Braithwaite is Co-Founder & CTO of RunReveal, the security data platform with real-time monitoring, built-in detections, and AI-powered investigations. Today, they manage and analyze security logs for teams at Harvey, ClickHouse, Cloudflare, and Temporal. RunReveal has multiple open source projects including event stream processing library kawa and query language pql. RunReveal has raised from investors including Costanoa, Modern Technical Fund, and Runtime Ventures. In this episode, we dig into:Why today's modern security teams are rethinking data management The benefits of building RunReveal on ClickHouse How they worked with early believers / customers like TemporalTheir open source strategy and building trust with the community through open sourcing components like their event processing libraryTheir MCP server and enabling security teams to use AI to automate investigations (including the launch of their new remote MCP server)

В этом выпуске: вайбкодинг и переезд на Linux, агенты и bloom фильтры в ClickHouse. [00:01:18] Чему мы научились за неделю Using Bloom filter indexes for real-time text search in ClickHouse PSI — Pressure Stall Information — The Linux Kernel documentation [00:30:46] Саша свинтил на Linux [00:51:41] Project Vend: Can Claude run a small shop? (And… Читать далее →

Guest Alan Braithwaite, Co-founder and CTO @ RunReveal Topics: SIEM is hard, and many vendors have discovered this over the years. You need to get storage, security and integration complexity just right. You also need to be better than incumbents. How would you approach this now? Decoupled SIEM vs SIEM/EDR/XDR combo. These point in the opposite directions, which side do you think will win? In a world where data volumes are exploding, especially in cloud environments, you're building a SIEM with ClickHouse as its backend, focusing on both parsed and raw logs. What's the core advantage of this approach, and how does it address the limitations of traditional SIEMs in handling scale?  Cribl, Bindplane and “security pipeline vendors” are all the rage. Won't it be logical to just include this into a modern SIEM? You're envisioning a 'Pipeline QL' that compiles to SQL, enabling 'detection in SQL.' This sounds like a significant shift, and perhaps not to the better? (Anton is horrified, for once) How does this approach affect detection engineering? With Sigma HQ support out-of-the-box, and the ability to convert SPL to Sigma, you're clearly aiming for interoperability. How crucial is this approach in your vision, and how do you see it benefiting the security community? What is SIEM in 2025 and beyond?  What's the endgame for security telemetry data? Is this truly SIEM 3.0, 4.0 or whatever-oh? Resources: EP197 SIEM (Decoupled or Not), and Security Data Lakes: A Google SecOps Perspective EP123 The Good, the Bad, and the Epic of Threat Detection at Scale with Panther EP190 Unraveling the Security Data Fabric: Need, Benefits, and Futures “20 Years of SIEM: Celebrating My Dubious Anniversary” blog “RSA 2025: AI's Promise vs. Security's Past — A Reality Check” blog tl;dr security newsletter Introducing a RunReveal Model Context Protocol Server! MCP: Building Your SecOps AI Ecosystem AI Runbooks for Google SecOps: Security Operations with Model Context Protocol  

This week on the PHP Podcast, Eric, John, and special guest Scott Keck-Warren talk about PHP Tek 2025 Wrap Up, NativePHP, Ethics in Web Development, and more… Links from the show: Security Starts With Developer Enablement: Lessons From PHP TEK 2025 The Bucket | Post PHP[TEK] Reflections Slightly Caffeinated | PHPTek, AI workshop, and ClickHouse […] The post The PHP Podcast: 2025.05.29 appeared first on PHP Architect.

The ClickHouse® project is a rising star in observability and analytics, challenging performance conventions with its breakneck speed. This open source OLAP column store, originally developed at Yandex to power their web analytics platform at massive scale, has quickly evolved into one of the hottest open source observability data stores around. Its published performance benchmarks have been the topic of conversation, outperforming many legacy databases and setting a new bar for fast queries over large volumes of data.Our guest for this episode is Robert Hodges, CEO of Altinity — the second largest contributor to the ClickHouse project. With over 30 years of experience in databases, Robert brings deep insights into how ClickHouse is challenging legacy databases at scale. We'll also explore Altinity's just-launched groundbreaking open source project—Project Antalya—which extends ClickHouse with Apache Iceberg shared storage, unlocking dramatic improvements in both performance and cost efficiency. Think 90% reductions in storage costs and 10 to 100x faster queries, all without requiring any changes to your existing applications.The episode was live-streamed on 20 May 2025 and the video is available at https://www.youtube.com/watch?v=VeyTL2JlWp0You can read the recap post: https://medium.com/p/2004160b2f5e/ OpenObservability Talks episodes are released monthly, on the last Thursday of each month and are available for listening on your favorite podcast app and on YouTube.We live-stream the episodes on Twitch and YouTube Live - tune in to see us live, and chime in with your comments and questions on the live chat.⁠⁠https://www.youtube.com/@openobservabilitytalks⁠  https://www.twitch.tv/openobservability⁠Show Notes:00:00 - Intro01:38 - ClickHouse elevator pitch02:46 - guest intro04:48 - ClickHouse under the hood08:15 - SQL and the database evolution path 11:20 - the return of SQL16:13 - design for speed 17:14 - use cases for ClickHouse19:18 - ClickHouse ecosystem22:22 - ClickHouse on Kubernetes 31:45 - know how ClickHouse works inside to get the most out of it 38:59 - ClickHouse for Observability46:58 - Project Antalya55:03 - Kubernetes 1.33 release55:32 - OpenSearch 3.0 release56:01 - New Permissive License for ML Models Announced by the Linux Foundation57:08 - OutroResources:ClickHouse on GitHub: https://github.com/ClickHouse/ClickHouse Shopify's Journey to Planet-Scale Observability: https://medium.com/p/9c0b299a04ddProject Antalya: https://altinity.com/blog/getting-started-with-altinitys-project-antalya https://cmtops.dev/posts/building-observability-with-clickhouse/ Kubernetes 1.33 release highlights: https://www.linkedin.com/feed/update/urn:li:activity:7321054742174924800/ New Permissive License for Machine Learning Models Announced by the Linux Foundation: https://www.linkedin.com/feed/update/urn:li:share:7331046183244611584  Opensearch 3.0 major release: https://www.linkedin.com/posts/horovits_opensearch-activity-7325834736008880128-kCqrSocials:Twitter:⁠ https://twitter.com/OpenObserv⁠YouTube: ⁠https://www.youtube.com/@openobservabilitytalks⁠Dotan Horovits============X (Twitter): @horovitsLinkedIn: www.linkedin.com/in/horovitsMastodon: @horovits@fosstodonBlueSky: @horovits.bsky.socialRobert Hodges=============LinkedIn: https://www.linkedin.com/in/berkeleybob2105/ 

In this episode, Bill Banham talks with Marina Farthouat, the new Vice President, People at Oyster - the global employment platform that enables companies to hire, pay, and care for distributed teams. Marina brings a refreshing perspective to the HRchat Show about transforming workplace norms through remote and distributed teams. Drawing from her diverse background spanning investment banking to startups, Marina shares why her most connected workplace experiences have consistently been in remote organizations."Talent doesn't have a nationality," Marina asserts, challenging traditional location-based hiring approaches. She makes a compelling case for distributed teams as both a talent strategy and a resilience measure. When recent blackouts hit Spain and Portugal, Oyster's globally distributed workforce demonstrated exactly this kind of operational continuity—shifting activities seamlessly to unaffected regions.What makes Marina's perspective particularly valuable is her holistic view of remote work benefits. Beyond the usual flexibility talking points, she highlights how remote arrangements positively impact families, especially children who no longer lose parents to long commutes. She dismantles the myth that remote workers feel disconnected, explaining how digital platforms actually create more egalitarian access to leadership and information than traditional office environments where proximity matters.The conversation tackles economic concerns head-on, addressing fears that global hiring simply shifts jobs to lower-cost regions. Marina offers a more nuanced view: global talent acquisition isn't about replacement but expansion and resilience. She emphasizes that startups particularly benefit from access to diverse talent pools while managing burn rates effectively.By the way, if you enjoy this conversation and want to learn more about Marina's team and some of the people challenges they tackle, check out episode 338 with Oyster Co-founder Jack Mardack.Marina leads the company's people strategy with a focus on building a human-centric, inclusive, and sustainable culture across a global workforce.She brings a wealth of experience leading People functions in remote organizations, most recently at ClickHouse and Elastic. Marina has developed deep expertise in employee engagement and organizational development, as well as in building strong cultures and scaling global comSupport the showFeature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here. Follow us on LinkedIn Subscribe to our newsletter Check out our in-person events

Highlights from this week's conversation include:Background of ClickHouse (1:14)PostgreSQL Data Replication Tool (3:19)Emerging Technologies Observations (7:25)Observability and Market Dynamics (11:26)Product Development Challenges (12:39)Challenges with PostgreSQL Performance (15:30)Philosophy of Open Source (18:01)Open Source Advantages (22:56)Simplified Stack Vision (24:48)End-to-End Use Cases (28:13)Migration Strategies (30:21)Final Thoughts and Takeaways (33:29)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Shopify operates at massive scale, running thousands of services and processing billions of events per second. To tackle the challenges of observability at this scale, they built Observe—an in-house observability stack that makes use of open-source tools and specifications. In fact, they replaced an older vendors-based system, in an awe-inspiring migration project. But why build their own stack? Which open source tools did they use? How did they shape the user experience to their needs?Joining us to unpack Shopify's journey is Elijah McPherson, an engineering leader with deep expertise in observability and distributed systems. Elijah led the complete rebuild of Shopify's observability stack and now also oversees jobs, caching, search, and ClickHouse infrastructure. Tune in to hear firsthand insights from one of the most innovative purpose-built observability implementations in production today!The episode was live-streamed on 11 February 2025 and the video is available at https://www.youtube.com/watch?v=rBfTjlXKJW0OpenObservability Talks episodes are released monthly, on the last Thursday of each month and are available for listening on your favorite podcast app and on YouTube.We live-stream the episodes on Twitch and YouTube Live - tune in to see us live, and chime in with your comments and questions on the live chat.⁠⁠https://www.youtube.com/@openobservabilitytalks⁠  https://www.twitch.tv/openobservability⁠Show Notes:00:46 - Episode and guest intro03:43 - Why rebuild the observability stack in house 05:47 - Cost and vendor lock-in07:09 - Tailoring observability for the organizational processes10:27 - How to build a team to build in-house observability 13:37 - The importance of product sense in internal platforms18:05 - The functionality of Shopify's observability platform 25:15 - The Open Source stack used at Shopify observability 29:50 - Extending open source Grafana to Shopify's needs36:23 - Adopting open standards 42:26 - observability into business health45:16 - how to run a migration project for a live production platform53:15 - final tips and best practices 56:41 - which organizations should develop in-house observabilityResources: Episode: Scaling Platform Engineering: Shopify's Blueprint: https://medium.com/p/f18e97140681  Shopify Observe - lectures: https://www.linkedin.com/posts/elijahmcpherson_observe-activity-7258195493657223168-mOGS/ Socials:Twitter:⁠ https://twitter.com/OpenObserv⁠YouTube: ⁠https://www.youtube.com/@openobservabilitytalks⁠Dotan Horovits============Twitter:@horovitsLinkedIn:www.linkedin.com/in/horovitsMastodon: @horovits@fosstodonBlueSky: @horovits.bsky.socialElijah McPherson===============Twitter: https://twitter.com/ElijahMcPhersonLinkedIn: https://www.linkedin.com/in/elijahmcpherson/

Did you know that adding a simple Code Interpreter took o3 from 9.2% to 32% on FrontierMath? The Latent Space crew is hosting a hack night Feb 11th in San Francisco focused on CodeGen use cases, co-hosted with E2B and Edge AGI; watch E2B's new workshop and RSVP here!We're happy to announce that today's guest Samuel Colvin will be teaching his very first Pydantic AI workshop at the newly announced AI Engineer NYC Workshops day on Feb 22! 25 tickets left.If you're a Python developer, it's very likely that you've heard of Pydantic. Every month, it's downloaded >300,000,000 times, making it one of the top 25 PyPi packages. OpenAI uses it in its SDK for structured outputs, it's at the core of FastAPI, and if you've followed our AI Engineer Summit conference, Jason Liu of Instructor has given two great talks about it: “Pydantic is all you need” and “Pydantic is STILL all you need”. Now, Samuel Colvin has raised $17M from Sequoia to turn Pydantic from an open source project to a full stack AI engineer platform with Logfire, their observability platform, and PydanticAI, their new agent framework.Logfire: bringing OTEL to AIOpenTelemetry recently merged Semantic Conventions for LLM workloads which provides standard definitions to track performance like gen_ai.server.time_per_output_token. In Sam's view at least 80% of new apps being built today have some sort of LLM usage in them, and just like web observability platform got replaced by cloud-first ones in the 2010s, Logfire wants to do the same for AI-first apps. If you're interested in the technical details, Logfire migrated away from Clickhouse to Datafusion for their backend. We spent some time on the importance of picking open source tools you understand and that you can actually contribute to upstream, rather than the more popular ones; listen in ~43:19 for that part.Agents are the killer app for graphsPydantic AI is their attempt at taking a lot of the learnings that LangChain and the other early LLM frameworks had, and putting Python best practices into it. At an API level, it's very similar to the other libraries: you can call LLMs, create agents, do function calling, do evals, etc.They define an “Agent” as a container with a system prompt, tools, structured result, and an LLM. Under the hood, each Agent is now a graph of function calls that can orchestrate multi-step LLM interactions. You can start simple, then move toward fully dynamic graph-based control flow if needed.“We were compelled enough by graphs once we got them right that our agent implementation [...] is now actually a graph under the hood.”Why Graphs?* More natural for complex or multi-step AI workflows.* Easy to visualize and debug with mermaid diagrams.* Potential for distributed runs, or “waiting days” between steps in certain flows.In parallel, you see folks like Emil Eifrem of Neo4j talk about GraphRAG as another place where graphs fit really well in the AI stack, so it might be time for more people to take them seriously.Full Video EpisodeLike and subscribe!Chapters* 00:00:00 Introductions* 00:00:24 Origins of Pydantic* 00:05:28 Pydantic's AI moment * 00:08:05 Why build a new agents framework?* 00:10:17 Overview of Pydantic AI* 00:12:33 Becoming a believer in graphs* 00:24:02 God Model vs Compound AI Systems* 00:28:13 Why not build an LLM gateway?* 00:31:39 Programmatic testing vs live evals* 00:35:51 Using OpenTelemetry for AI traces* 00:43:19 Why they don't use Clickhouse* 00:48:34 Competing in the observability space* 00:50:41 Licensing decisions for Pydantic and LogFire* 00:51:48 Building Pydantic.run* 00:55:24 Marimo and the future of Jupyter notebooks* 00:57:44 London's AI sceneShow Notes* Sam Colvin* Pydantic* Pydantic AI* Logfire* Pydantic.run* Zod* E2B* Arize* Langsmith* Marimo* Prefect* GLA (Google Generative Language API)* OpenTelemetry* Jason Liu* Sebastian Ramirez* Bogomil Balkansky* Hood Chatham* Jeremy Howard* Andrew LambTranscriptAlessio [00:00:03]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:12]: Good morning. And today we're very excited to have Sam Colvin join us from Pydantic AI. Welcome. Sam, I heard that Pydantic is all we need. Is that true?Samuel [00:00:24]: I would say you might need Pydantic AI and Logfire as well, but it gets you a long way, that's for sure.Swyx [00:00:29]: Pydantic almost basically needs no introduction. It's almost 300 million downloads in December. And obviously, in the previous podcasts and discussions we've had with Jason Liu, he's been a big fan and promoter of Pydantic and AI.Samuel [00:00:45]: Yeah, it's weird because obviously I didn't create Pydantic originally for uses in AI, it predates LLMs. But it's like we've been lucky that it's been picked up by that community and used so widely.Swyx [00:00:58]: Actually, maybe we'll hear it. Right from you, what is Pydantic and maybe a little bit of the origin story?Samuel [00:01:04]: The best name for it, which is not quite right, is a validation library. And we get some tension around that name because it doesn't just do validation, it will do coercion by default. We now have strict mode, so you can disable that coercion. But by default, if you say you want an integer field and you get in a string of 1, 2, 3, it will convert it to 123 and a bunch of other sensible conversions. And as you can imagine, the semantics around it. Exactly when you convert and when you don't, it's complicated, but because of that, it's more than just validation. Back in 2017, when I first started it, the different thing it was doing was using type hints to define your schema. That was controversial at the time. It was genuinely disapproved of by some people. I think the success of Pydantic and libraries like FastAPI that build on top of it means that today that's no longer controversial in Python. And indeed, lots of other people have copied that route, but yeah, it's a data validation library. It uses type hints for the for the most part and obviously does all the other stuff you want, like serialization on top of that. But yeah, that's the core.Alessio [00:02:06]: Do you have any fun stories on how JSON schemas ended up being kind of like the structure output standard for LLMs? And were you involved in any of these discussions? Because I know OpenAI was, you know, one of the early adopters. So did they reach out to you? Was there kind of like a structure output console in open source that people were talking about or was it just a random?Samuel [00:02:26]: No, very much not. So I originally. Didn't implement JSON schema inside Pydantic and then Sebastian, Sebastian Ramirez, FastAPI came along and like the first I ever heard of him was over a weekend. I got like 50 emails from him or 50 like emails as he was committing to Pydantic, adding JSON schema long pre version one. So the reason it was added was for OpenAPI, which is obviously closely akin to JSON schema. And then, yeah, I don't know why it was JSON that got picked up and used by OpenAI. It was obviously very convenient for us. That's because it meant that not only can you do the validation, but because Pydantic will generate you the JSON schema, it will it kind of can be one source of source of truth for structured outputs and tools.Swyx [00:03:09]: Before we dive in further on the on the AI side of things, something I'm mildly curious about, obviously, there's Zod in JavaScript land. Every now and then there is a new sort of in vogue validation library that that takes over for quite a few years and then maybe like some something else comes along. Is Pydantic? Is it done like the core Pydantic?Samuel [00:03:30]: I've just come off a call where we were redesigning some of the internal bits. There will be a v3 at some point, which will not break people's code half as much as v2 as in v2 was the was the massive rewrite into Rust, but also fixing all the stuff that was broken back from like version zero point something that we didn't fix in v1 because it was a side project. We have plans to move some of the basically store the data in Rust types after validation. Not completely. So we're still working to design the Pythonic version of it, in order for it to be able to convert into Python types. So then if you were doing like validation and then serialization, you would never have to go via a Python type we reckon that can give us somewhere between three and five times another three to five times speed up. That's probably the biggest thing. Also, like changing how easy it is to basically extend Pydantic and define how particular types, like for example, NumPy arrays are validated and serialized. But there's also stuff going on. And for example, Jitter, the JSON library in Rust that does the JSON parsing, has SIMD implementation at the moment only for AMD64. So we can add that. We need to go and add SIMD for other instruction sets. So there's a bunch more we can do on performance. I don't think we're going to go and revolutionize Pydantic, but it's going to continue to get faster, continue, hopefully, to allow people to do more advanced things. We might add a binary format like CBOR for serialization for when you'll just want to put the data into a database and probably load it again from Pydantic. So there are some things that will come along, but for the most part, it should just get faster and cleaner.Alessio [00:05:04]: From a focus perspective, I guess, as a founder too, how did you think about the AI interest rising? And then how do you kind of prioritize, okay, this is worth going into more, and we'll talk about Pydantic AI and all of that. What was maybe your early experience with LLAMP, and when did you figure out, okay, this is something we should take seriously and focus more resources on it?Samuel [00:05:28]: I'll answer that, but I'll answer what I think is a kind of parallel question, which is Pydantic's weird, because Pydantic existed, obviously, before I was starting a company. I was working on it in my spare time, and then beginning of 22, I started working on the rewrite in Rust. And I worked on it full-time for a year and a half, and then once we started the company, people came and joined. And it was a weird project, because that would never go away. You can't get signed off inside a startup. Like, we're going to go off and three engineers are going to work full-on for a year in Python and Rust, writing like 30,000 lines of Rust just to release open-source-free Python library. The result of that has been excellent for us as a company, right? As in, it's made us remain entirely relevant. And it's like, Pydantic is not just used in the SDKs of all of the AI libraries, but I can't say which one, but one of the big foundational model companies, when they upgraded from Pydantic v1 to v2, their number one internal model... The metric of performance is time to first token. That went down by 20%. So you think about all of the actual AI going on inside, and yet at least 20% of the CPU, or at least the latency inside requests was actually Pydantic, which shows like how widely it's used. So we've benefited from doing that work, although it didn't, it would have never have made financial sense in most companies. In answer to your question about like, how do we prioritize AI, I mean, the honest truth is we've spent a lot of the last year and a half building. Good general purpose observability inside LogFire and making Pydantic good for general purpose use cases. And the AI has kind of come to us. Like we just, not that we want to get away from it, but like the appetite, uh, both in Pydantic and in LogFire to go and build with AI is enormous because it kind of makes sense, right? Like if you're starting a new greenfield project in Python today, what's the chance that you're using GenAI 80%, let's say, globally, obviously it's like a hundred percent in California, but even worldwide, it's probably 80%. Yeah. And so everyone needs that stuff. And there's so much yet to be figured out so much like space to do things better in the ecosystem in a way that like to go and implement a database that's better than Postgres is a like Sisyphean task. Whereas building, uh, tools that are better for GenAI than some of the stuff that's about now is not very difficult. Putting the actual models themselves to one side.Alessio [00:07:40]: And then at the same time, then you released Pydantic AI recently, which is, uh, um, you know, agent framework and early on, I would say everybody like, you know, Langchain and like, uh, Pydantic kind of like a first class support, a lot of these frameworks, we're trying to use you to be better. What was the decision behind we should do our own framework? Were there any design decisions that you disagree with any workloads that you think people didn't support? Well,Samuel [00:08:05]: it wasn't so much like design and workflow, although I think there were some, some things we've done differently. Yeah. I think looking in general at the ecosystem of agent frameworks, the engineering quality is far below that of the rest of the Python ecosystem. There's a bunch of stuff that we have learned how to do over the last 20 years of building Python libraries and writing Python code that seems to be abandoned by people when they build agent frameworks. Now I can kind of respect that, particularly in the very first agent frameworks, like Langchain, where they were literally figuring out how to go and do this stuff. It's completely understandable that you would like basically skip some stuff.Samuel [00:08:42]: I'm shocked by the like quality of some of the agent frameworks that have come out recently from like well-respected names, which it just seems to be opportunism and I have little time for that, but like the early ones, like I think they were just figuring out how to do stuff and just as lots of people have learned from Pydantic, we were able to learn a bit from them. I think from like the gap we saw and the thing we were frustrated by was the production readiness. And that means things like type checking, even if type checking makes it hard. Like Pydantic AI, I will put my hand up now and say it has a lot of generics and you need to, it's probably easier to use it if you've written a bit of Rust and you really understand generics, but like, and that is, we're not claiming that that makes it the easiest thing to use in all cases, we think it makes it good for production applications in big systems where type checking is a no-brainer in Python. But there are also a bunch of stuff we've learned from maintaining Pydantic over the years that we've gone and done. So every single example in Pydantic AI's documentation is run on Python. As part of tests and every single print output within an example is checked during tests. So it will always be up to date. And then a bunch of things that, like I say, are standard best practice within the rest of the Python ecosystem, but I'm not followed surprisingly by some AI libraries like coverage, linting, type checking, et cetera, et cetera, where I think these are no-brainers, but like weirdly they're not followed by some of the other libraries.Alessio [00:10:04]: And can you just give an overview of the framework itself? I think there's kind of like the. LLM calling frameworks, there are the multi-agent frameworks, there's the workflow frameworks, like what does Pydantic AI do?Samuel [00:10:17]: I glaze over a bit when I hear all of the different sorts of frameworks, but I like, and I will tell you when I built Pydantic, when I built Logfire and when I built Pydantic AI, my methodology is not to go and like research and review all of the other things. I kind of work out what I want and I go and build it and then feedback comes and we adjust. So the fundamental building block of Pydantic AI is agents. The exact definition of agents and how you want to define them. is obviously ambiguous and our things are probably sort of agent-lit, not that we would want to go and rename them to agent-lit, but like the point is you probably build them together to build something and most people will call an agent. So an agent in our case has, you know, things like a prompt, like system prompt and some tools and a structured return type if you want it, that covers the vast majority of cases. There are situations where you want to go further and the most complex workflows where you want graphs and I resisted graphs for quite a while. I was sort of of the opinion you didn't need them and you could use standard like Python flow control to do all of that stuff. I had a few arguments with people, but I basically came around to, yeah, I can totally see why graphs are useful. But then we have the problem that by default, they're not type safe because if you have a like add edge method where you give the names of two different edges, there's no type checking, right? Even if you go and do some, I'm not, not all the graph libraries are AI specific. So there's a, there's a graph library called, but it allows, it does like a basic runtime type checking. Ironically using Pydantic to try and make up for the fact that like fundamentally that graphs are not typed type safe. Well, I like Pydantic, but it did, that's not a real solution to have to go and run the code to see if it's safe. There's a reason that starting type checking is so powerful. And so we kind of, from a lot of iteration eventually came up with a system of using normally data classes to define nodes where you return the next node you want to call and where we're able to go and introspect the return type of a node to basically build the graph. And so the graph is. Yeah. Inherently type safe. And once we got that right, I, I wasn't, I'm incredibly excited about graphs. I think there's like masses of use cases for them, both in gen AI and other development, but also software's all going to have interact with gen AI, right? It's going to be like web. There's no longer be like a web department in a company is that there's just like all the developers are building for web building with databases. The same is going to be true for gen AI.Alessio [00:12:33]: Yeah. I see on your docs, you call an agent, a container that contains a system prompt function. Tools, structure, result, dependency type model, and then model settings. Are the graphs in your mind, different agents? Are they different prompts for the same agent? What are like the structures in your mind?Samuel [00:12:52]: So we were compelled enough by graphs once we got them right, that we actually merged the PR this morning. That means our agent implementation without changing its API at all is now actually a graph under the hood as it is built using our graph library. So graphs are basically a lower level tool that allow you to build these complex workflows. Our agents are technically one of the many graphs you could go and build. And we just happened to build that one for you because it's a very common, commonplace one. But obviously there are cases where you need more complex workflows where the current agent assumptions don't work. And that's where you can then go and use graphs to build more complex things.Swyx [00:13:29]: You said you were cynical about graphs. What changed your mind specifically?Samuel [00:13:33]: I guess people kept giving me examples of things that they wanted to use graphs for. And my like, yeah, but you could do that in standard flow control in Python became a like less and less compelling argument to me because I've maintained those systems that end up with like spaghetti code. And I could see the appeal of this like structured way of defining the workflow of my code. And it's really neat that like just from your code, just from your type hints, you can get out a mermaid diagram that defines exactly what can go and happen.Swyx [00:14:00]: Right. Yeah. You do have very neat implementation of sort of inferring the graph from type hints, I guess. Yeah. Is what I would call it. Yeah. I think the question always is I have gone back and forth. I used to work at Temporal where we would actually spend a lot of time complaining about graph based workflow solutions like AWS step functions. And we would actually say that we were better because you could use normal control flow that you already knew and worked with. Yours, I guess, is like a little bit of a nice compromise. Like it looks like normal Pythonic code. But you just have to keep in mind what the type hints actually mean. And that's what we do with the quote unquote magic that the graph construction does.Samuel [00:14:42]: Yeah, exactly. And if you look at the internal logic of actually running a graph, it's incredibly simple. It's basically call a node, get a node back, call that node, get a node back, call that node. If you get an end, you're done. We will add in soon support for, well, basically storage so that you can store the state between each node that's run. And then the idea is you can then distribute the graph and run it across computers. And also, I mean, the other weird, the other bit that's really valuable is across time. Because it's all very well if you look at like lots of the graph examples that like Claude will give you. If it gives you an example, it gives you this lovely enormous mermaid chart of like the workflow, for example, managing returns if you're an e-commerce company. But what you realize is some of those lines are literally one function calls another function. And some of those lines are wait six days for the customer to print their like piece of paper and put it in the post. And if you're writing like your demo. Project or your like proof of concept, that's fine because you can just say, and now we call this function. But when you're building when you're in real in real life, that doesn't work. And now how do we manage that concept to basically be able to start somewhere else in the in our code? Well, this graph implementation makes it incredibly easy because you just pass the node that is the start point for carrying on the graph and it continues to run. So it's things like that where I was like, yeah, I can just imagine how things I've done in the past would be fundamentally easier to understand if we had done them with graphs.Swyx [00:16:07]: You say imagine, but like right now, this pedantic AI actually resume, you know, six days later, like you said, or is this just like a theoretical thing we can go someday?Samuel [00:16:16]: I think it's basically Q&A. So there's an AI that's asking the user a question and effectively you then call the CLI again to continue the conversation. And it basically instantiates the node and calls the graph with that node again. Now, we don't have the logic yet for effectively storing state in the database between individual nodes that we're going to add soon. But like the rest of it is basically there.Swyx [00:16:37]: It does make me think that not only are you competing with Langchain now and obviously Instructor, and now you're going into sort of the more like orchestrated things like Airflow, Prefect, Daxter, those guys.Samuel [00:16:52]: Yeah, I mean, we're good friends with the Prefect guys and Temporal have the same investors as us. And I'm sure that my investor Bogomol would not be too happy if I was like, oh, yeah, by the way, as well as trying to take on Datadog. We're also going off and trying to take on Temporal and everyone else doing that. Obviously, we're not doing all of the infrastructure of deploying that right yet, at least. We're, you know, we're just building a Python library. And like what's crazy about our graph implementation is, sure, there's a bit of magic in like introspecting the return type, you know, extracting things from unions, stuff like that. But like the actual calls, as I say, is literally call a function and get back a thing and call that. It's like incredibly simple and therefore easy to maintain. The question is, how useful is it? Well, I don't know yet. I think we have to go and find out. We have a whole. We've had a slew of people joining our Slack over the last few days and saying, tell me how good Pydantic AI is. How good is Pydantic AI versus Langchain? And I refuse to answer. That's your job to go and find that out. Not mine. We built a thing. I'm compelled by it, but I'm obviously biased. The ecosystem will work out what the useful tools are.Swyx [00:17:52]: Bogomol was my board member when I was at Temporal. And I think I think just generally also having been a workflow engine investor and participant in this space, it's a big space. Like everyone needs different functions. I think the one thing that I would say like yours, you know, as a library, you don't have that much control of it over the infrastructure. I do like the idea that each new agents or whatever or unit of work, whatever you call that should spin up in this sort of isolated boundaries. Whereas yours, I think around everything runs in the same process. But you ideally want to sort of spin out its own little container of things.Samuel [00:18:30]: I agree with you a hundred percent. And we will. It would work now. Right. As in theory, you're just like as long as you can serialize the calls to the next node, you just have to all of the different containers basically have to have the same the same code. I mean, I'm super excited about Cloudflare workers running Python and being able to install dependencies. And if Cloudflare could only give me my invitation to the private beta of that, we would be exploring that right now because I'm super excited about that as a like compute level for some of this stuff where exactly what you're saying, basically. You can run everything as an individual. Like worker function and distribute it. And it's resilient to failure, et cetera, et cetera.Swyx [00:19:08]: And it spins up like a thousand instances simultaneously. You know, you want it to be sort of truly serverless at once. Actually, I know we have some Cloudflare friends who are listening, so hopefully they'll get in front of the line. Especially.Samuel [00:19:19]: I was in Cloudflare's office last week shouting at them about other things that frustrate me. I have a love-hate relationship with Cloudflare. Their tech is awesome. But because I use it the whole time, I then get frustrated. So, yeah, I'm sure I will. I will. I will get there soon.Swyx [00:19:32]: There's a side tangent on Cloudflare. Is Python supported at full? I actually wasn't fully aware of what the status of that thing is.Samuel [00:19:39]: Yeah. So Pyodide, which is Python running inside the browser in scripting, is supported now by Cloudflare. They basically, they're having some struggles working out how to manage, ironically, dependencies that have binaries, in particular, Pydantic. Because these workers where you can have thousands of them on a given metal machine, you don't want to have a difference. You basically want to be able to have a share. Shared memory for all the different Pydantic installations, effectively. That's the thing they work out. They're working out. But Hood, who's my friend, who is the primary maintainer of Pyodide, works for Cloudflare. And that's basically what he's doing, is working out how to get Python running on Cloudflare's network.Swyx [00:20:19]: I mean, the nice thing is that your binary is really written in Rust, right? Yeah. Which also compiles the WebAssembly. Yeah. So maybe there's a way that you'd build... You have just a different build of Pydantic and that ships with whatever your distro for Cloudflare workers is.Samuel [00:20:36]: Yes, that's exactly what... So Pyodide has builds for Pydantic Core and for things like NumPy and basically all of the popular binary libraries. Yeah. It's just basic. And you're doing exactly that, right? You're using Rust to compile the WebAssembly and then you're calling that shared library from Python. And it's unbelievably complicated, but it works. Okay.Swyx [00:20:57]: Staying on graphs a little bit more, and then I wanted to go to some of the other features that you have in Pydantic AI. I see in your docs, there are sort of four levels of agents. There's single agents, there's agent delegation, programmatic agent handoff. That seems to be what OpenAI swarms would be like. And then the last one, graph-based control flow. Would you say that those are sort of the mental hierarchy of how these things go?Samuel [00:21:21]: Yeah, roughly. Okay.Swyx [00:21:22]: You had some expression around OpenAI swarms. Well.Samuel [00:21:25]: And indeed, OpenAI have got in touch with me and basically, maybe I'm not supposed to say this, but basically said that Pydantic AI looks like what swarms would become if it was production ready. So, yeah. I mean, like, yeah, which makes sense. Awesome. Yeah. I mean, in fact, it was specifically saying, how can we give people the same feeling that they were getting from swarms that led us to go and implement graphs? Because my, like, just call the next agent with Python code was not a satisfactory answer to people. So it was like, okay, we've got to go and have a better answer for that. It's not like, let us to get to graphs. Yeah.Swyx [00:21:56]: I mean, it's a minimal viable graph in some sense. What are the shapes of graphs that people should know? So the way that I would phrase this is I think Anthropic did a very good public service and also kind of surprisingly influential blog post, I would say, when they wrote Building Effective Agents. We actually have the authors coming to speak at my conference in New York, which I think you're giving a workshop at. Yeah.Samuel [00:22:24]: I'm trying to work it out. But yes, I think so.Swyx [00:22:26]: Tell me if you're not. yeah, I mean, like, that was the first, I think, authoritative view of, like, what kinds of graphs exist in agents and let's give each of them a name so that everyone is on the same page. So I'm just kind of curious if you have community names or top five patterns of graphs.Samuel [00:22:44]: I don't have top five patterns of graphs. I would love to see what people are building with them. But like, it's been it's only been a couple of weeks. And of course, there's a point is that. Because they're relatively unopinionated about what you can go and do with them. They don't suit them. Like, you can go and do lots of lots of things with them, but they don't have the structure to go and have like specific names as much as perhaps like some other systems do. I think what our agents are, which have a name and I can't remember what it is, but this basically system of like, decide what tool to call, go back to the center, decide what tool to call, go back to the center and then exit. One form of graph, which, as I say, like our agents are effectively one implementation of a graph, which is why under the hood they are now using graphs. And it'll be interesting to see over the next few years whether we end up with these like predefined graph names or graph structures or whether it's just like, yep, I built a graph or whether graphs just turn out not to match people's mental image of what they want and die away. We'll see.Swyx [00:23:38]: I think there is always appeal. Every developer eventually gets graph religion and goes, oh, yeah, everything's a graph. And then they probably over rotate and go go too far into graphs. And then they have to learn a whole bunch of DSLs. And then they're like, actually, I didn't need that. I need this. And they scale back a little bit.Samuel [00:23:55]: I'm at the beginning of that process. I'm currently a graph maximalist, although I haven't actually put any into production yet. But yeah.Swyx [00:24:02]: This has a lot of philosophical connections with other work coming out of UC Berkeley on compounding AI systems. I don't know if you know of or care. This is the Gartner world of things where they need some kind of industry terminology to sell it to enterprises. I don't know if you know about any of that.Samuel [00:24:24]: I haven't. I probably should. I should probably do it because I should probably get better at selling to enterprises. But no, no, I don't. Not right now.Swyx [00:24:29]: This is really the argument is that instead of putting everything in one model, you have more control and more maybe observability to if you break everything out into composing little models and changing them together. And obviously, then you need an orchestration framework to do that. Yeah.Samuel [00:24:47]: And it makes complete sense. And one of the things we've seen with agents is they work well when they work well. But when they. Even if you have the observability through log five that you can see what was going on, if you don't have a nice hook point to say, hang on, this is all gone wrong. You have a relatively blunt instrument of basically erroring when you exceed some kind of limit. But like what you need to be able to do is effectively iterate through these runs so that you can have your own control flow where you're like, OK, we've gone too far. And that's where one of the neat things about our graph implementation is you can basically call next in a loop rather than just running the full graph. And therefore, you have this opportunity to to break out of it. But yeah, basically, it's the same point, which is like if you have two bigger unit of work to some extent, whether or not it involves gen AI. But obviously, it's particularly problematic in gen AI. You only find out afterwards when you've spent quite a lot of time and or money when it's gone off and done done the wrong thing.Swyx [00:25:39]: Oh, drop on this. We're not going to resolve this here, but I'll drop this and then we can move on to the next thing. This is the common way that we we developers talk about this. And then the machine learning researchers look at us. And laugh and say, that's cute. And then they just train a bigger model and they wipe us out in the next training run. So I think there's a certain amount of we are fighting the bitter lesson here. We're fighting AGI. And, you know, when AGI arrives, this will all go away. Obviously, on Latent Space, we don't really discuss that because I think AGI is kind of this hand wavy concept that isn't super relevant. But I think we have to respect that. For example, you could do a chain of thoughts with graphs and you could manually orchestrate a nice little graph that does like. Reflect, think about if you need more, more inference time, compute, you know, that's the hot term now. And then think again and, you know, scale that up. Or you could train Strawberry and DeepSeq R1. Right.Samuel [00:26:32]: I saw someone saying recently, oh, they were really optimistic about agents because models are getting faster exponentially. And I like took a certain amount of self-control not to describe that it wasn't exponential. But my main point was. If models are getting faster as quickly as you say they are, then we don't need agents and we don't really need any of these abstraction layers. We can just give our model and, you know, access to the Internet, cross our fingers and hope for the best. Agents, agent frameworks, graphs, all of this stuff is basically making up for the fact that right now the models are not that clever. In the same way that if you're running a customer service business and you have loads of people sitting answering telephones, the less well trained they are, the less that you trust them, the more that you need to give them a script to go through. Whereas, you know, so if you're running a bank and you have lots of customer service people who you don't trust that much, then you tell them exactly what to say. If you're doing high net worth banking, you just employ people who you think are going to be charming to other rich people and set them off to go and have coffee with people. Right. And the same is true of models. The more intelligent they are, the less we need to tell them, like structure what they go and do and constrain the routes in which they take.Swyx [00:27:42]: Yeah. Yeah. Agree with that. So I'm happy to move on. So the other parts of Pydantic AI that are worth commenting on, and this is like my last rant, I promise. So obviously, every framework needs to do its sort of model adapter layer, which is, oh, you can easily swap from OpenAI to Cloud to Grok. You also have, which I didn't know about, Google GLA, which I didn't really know about until I saw this in your docs, which is generative language API. I assume that's AI Studio? Yes.Samuel [00:28:13]: Google don't have good names for it. So Vertex is very clear. That seems to be the API that like some of the things use, although it returns 503 about 20% of the time. So... Vertex? No. Vertex, fine. But the... Oh, oh. GLA. Yeah. Yeah.Swyx [00:28:28]: I agree with that.Samuel [00:28:29]: So we have, again, another example of like, well, I think we go the extra mile in terms of engineering is we run on every commit, at least commit to main, we run tests against the live models. Not lots of tests, but like a handful of them. Oh, okay. And we had a point last week where, yeah, GLA is a little bit better. GLA1 was failing every single run. One of their tests would fail. And we, I think we might even have commented out that one at the moment. So like all of the models fail more often than you might expect, but like that one seems to be particularly likely to fail. But Vertex is the same API, but much more reliable.Swyx [00:29:01]: My rant here is that, you know, versions of this appear in Langchain and every single framework has to have its own little thing, a version of that. I would put to you, and then, you know, this is, this can be agree to disagree. This is not needed in Pydantic AI. I would much rather you adopt a layer like Lite LLM or what's the other one in JavaScript port key. And that's their job. They focus on that one thing and they, they normalize APIs for you. All new models are automatically added and you don't have to duplicate this inside of your framework. So for example, if I wanted to use deep seek, I'm out of luck because Pydantic AI doesn't have deep seek yet.Samuel [00:29:38]: Yeah, it does.Swyx [00:29:39]: Oh, it does. Okay. I'm sorry. But you know what I mean? Should this live in your code or should it live in a layer that's kind of your API gateway that's a defined piece of infrastructure that people have?Samuel [00:29:49]: And I think if a company who are well known, who are respected by everyone had come along and done this at the right time, maybe we should have done it a year and a half ago and said, we're going to be the universal AI layer. That would have been a credible thing to do. I've heard varying reports of Lite LLM is the truth. And it didn't seem to have exactly the type safety that we needed. Also, as I understand it, and again, I haven't looked into it in great detail. Part of their business model is proxying the request through their, through their own system to do the generalization. That would be an enormous put off to an awful lot of people. Honestly, the truth is I don't think it is that much work unifying the model. I get where you're coming from. I kind of see your point. I think the truth is that everyone is centralizing around open AIs. Open AI's API is the one to do. So DeepSeq support that. Grok with OK support that. Ollama also does it. I mean, if there is that library right now, it's more or less the open AI SDK. And it's very high quality. It's well type checked. It uses Pydantic. So I'm biased. But I mean, I think it's pretty well respected anyway.Swyx [00:30:57]: There's different ways to do this. Because also, it's not just about normalizing the APIs. You have to do secret management and all that stuff.Samuel [00:31:05]: Yeah. And there's also. There's Vertex and Bedrock, which to one extent or another, effectively, they host multiple models, but they don't unify the API. But they do unify the auth, as I understand it. Although we're halfway through doing Bedrock. So I don't know about it that well. But they're kind of weird hybrids because they support multiple models. But like I say, the auth is centralized.Swyx [00:31:28]: Yeah, I'm surprised they don't unify the API. That seems like something that I would do. You know, we can discuss all this all day. There's a lot of APIs. I agree.Samuel [00:31:36]: It would be nice if there was a universal one that we didn't have to go and build.Alessio [00:31:39]: And I guess the other side of, you know, routing model and picking models like evals. How do you actually figure out which one you should be using? I know you have one. First of all, you have very good support for mocking in unit tests, which is something that a lot of other frameworks don't do. So, you know, my favorite Ruby library is VCR because it just, you know, it just lets me store the HTTP requests and replay them. That part I'll kind of skip. I think you are busy like this test model. We're like just through Python. You try and figure out what the model might respond without actually calling the model. And then you have the function model where people can kind of customize outputs. Any other fun stories maybe from there? Or is it just what you see is what you get, so to speak?Samuel [00:32:18]: On those two, I think what you see is what you get. On the evals, I think watch this space. I think it's something that like, again, I was somewhat cynical about for some time. Still have my cynicism about some of the well, it's unfortunate that so many different things are called evals. It would be nice if we could agree. What they are and what they're not. But look, I think it's a really important space. I think it's something that we're going to be working on soon, both in Pydantic AI and in LogFire to try and support better because it's like it's an unsolved problem.Alessio [00:32:45]: Yeah, you do say in your doc that anyone who claims to know for sure exactly how your eval should be defined can safely be ignored.Samuel [00:32:52]: We'll delete that sentence when we tell people how to do their evals.Alessio [00:32:56]: Exactly. I was like, we need we need a snapshot of this today. And so let's talk about eval. So there's kind of like the vibe. Yeah. So you have evals, which is what you do when you're building. Right. Because you cannot really like test it that many times to get statistical significance. And then there's the production eval. So you also have LogFire, which is kind of like your observability product, which I tried before. It's very nice. What are some of the learnings you've had from building an observability tool for LEMPs? And yeah, as people think about evals, even like what are the right things to measure? What are like the right number of samples that you need to actually start making decisions?Samuel [00:33:33]: I'm not the best person to answer that is the truth. So I'm not going to come in here and tell you that I think I know the answer on the exact number. I mean, we can do some back of the envelope statistics calculations to work out that like having 30 probably gets you most of the statistical value of having 200 for, you know, by definition, 15% of the work. But the exact like how many examples do you need? For example, that's a much harder question to answer because it's, you know, it's deep within the how models operate in terms of LogFire. One of the reasons we built LogFire the way we have and we allow you to write SQL directly against your data and we're trying to build the like powerful fundamentals of observability is precisely because we know we don't know the answers. And so allowing people to go and innovate on how they're going to consume that stuff and how they're going to process it is we think that's valuable. Because even if we come along and offer you an evals framework on top of LogFire, it won't be right in all regards. And we want people to be able to go and innovate and being able to write their own SQL connected to the API. And effectively query the data like it's a database with SQL allows people to innovate on that stuff. And that's what allows us to do it as well. I mean, we do a bunch of like testing what's possible by basically writing SQL directly against LogFire as any user could. I think the other the other really interesting bit that's going on in observability is OpenTelemetry is centralizing around semantic attributes for GenAI. So it's a relatively new project. A lot of it's still being added at the moment. But basically the idea that like. They unify how both SDKs and or agent frameworks send observability data to to any OpenTelemetry endpoint. And so, again, we can go and having that unification allows us to go and like basically compare different libraries, compare different models much better. That stuff's in a very like early stage of development. One of the things we're going to be working on pretty soon is basically, I suspect, GenAI will be the first agent framework that implements those semantic attributes properly. Because, again, we control and we can say this is important for observability, whereas most of the other agent frameworks are not maintained by people who are trying to do observability. With the exception of Langchain, where they have the observability platform, but they chose not to go down the OpenTelemetry route. So they're like plowing their own furrow. And, you know, they're a lot they're even further away from standardization.Alessio [00:35:51]: Can you maybe just give a quick overview of how OTEL ties into the AI workflows? There's kind of like the question of is, you know, a trace. And a span like a LLM call. Is it the agent? It's kind of like the broader thing you're tracking. How should people think about it?Samuel [00:36:06]: Yeah, so they have a PR that I think may have now been merged from someone at IBM talking about remote agents and trying to support this concept of remote agents within GenAI. I'm not particularly compelled by that because I don't think that like that's actually by any means the common use case. But like, I suppose it's fine for it to be there. The majority of the stuff in OTEL is basically defining how you would instrument. A given call to an LLM. So basically the actual LLM call, what data you would send to your telemetry provider, how you would structure that. Apart from this slightly odd stuff on remote agents, most of the like agent level consideration is not yet implemented in is not yet decided effectively. And so there's a bit of ambiguity. Obviously, what's good about OTEL is you can in the end send whatever attributes you like. But yeah, there's quite a lot of churn in that space and exactly how we store the data. I think that one of the most interesting things, though, is that if you think about observability. Traditionally, it was sure everyone would say our observability data is very important. We must keep it safe. But actually, companies work very hard to basically not have anything that sensitive in their observability data. So if you're a doctor in a hospital and you search for a drug for an STI, the sequel might be sent to the observability provider. But none of the parameters would. It wouldn't have the patient number or their name or the drug. With GenAI, that distinction doesn't exist because it's all just messed up in the text. If you have that same patient asking an LLM how to. What drug they should take or how to stop smoking. You can't extract the PII and not send it to the observability platform. So the sensitivity of the data that's going to end up in observability platforms is going to be like basically different order of magnitude to what's in what you would normally send to Datadog. Of course, you can make a mistake and send someone's password or their card number to Datadog. But that would be seen as a as a like mistake. Whereas in GenAI, a lot of data is going to be sent. And I think that's why companies like Langsmith and are trying hard to offer observability. On prem, because there's a bunch of companies who are happy for Datadog to be cloud hosted, but want self-hosted self-hosting for this observability stuff with GenAI.Alessio [00:38:09]: And are you doing any of that today? Because I know in each of the spans you have like the number of tokens, you have the context, you're just storing everything. And then you're going to offer kind of like a self-hosting for the platform, basically. Yeah. Yeah.Samuel [00:38:23]: So we have scrubbing roughly equivalent to what the other observability platforms have. So if we, you know, if we see password as the key, we won't send the value. But like, like I said, that doesn't really work in GenAI. So we're accepting we're going to have to store a lot of data and then we'll offer self-hosting for those people who can afford it and who need it.Alessio [00:38:42]: And then this is, I think, the first time that most of the workloads performance is depending on a third party. You know, like if you're looking at Datadog data, usually it's your app that is driving the latency and like the memory usage and all of that. Here you're going to have spans that maybe take a long time to perform because the GLA API is not working or because OpenAI is kind of like overwhelmed. Do you do anything there since like the provider is almost like the same across customers? You know, like, are you trying to surface these things for people and say, hey, this was like a very slow span, but actually all customers using OpenAI right now are seeing the same thing. So maybe don't worry about it or.Samuel [00:39:20]: Not yet. We do a few things that people don't generally do in OTA. So we send. We send information at the beginning. At the beginning of a trace as well as sorry, at the beginning of a span, as well as when it finishes. By default, OTA only sends you data when the span finishes. So if you think about a request which might take like 20 seconds, even if some of the intermediate spans finished earlier, you can't basically place them on the page until you get the top level span. And so if you're using standard OTA, you can't show anything until those requests are finished. When those requests are taking a few hundred milliseconds, it doesn't really matter. But when you're doing Gen AI calls or when you're like running a batch job that might take 30 minutes. That like latency of not being able to see the span is like crippling to understanding your application. And so we've we do a bunch of slightly complex stuff to basically send data about a span as it starts, which is closely related. Yeah.Alessio [00:40:09]: Any thoughts on all the other people trying to build on top of OpenTelemetry in different languages, too? There's like the OpenLEmetry project, which doesn't really roll off the tongue. But how do you see the future of these kind of tools? Is everybody going to have to build? Why does everybody want to build? They want to build their own open source observability thing to then sell?Samuel [00:40:29]: I mean, we are not going off and trying to instrument the likes of the OpenAI SDK with the new semantic attributes, because at some point that's going to happen and it's going to live inside OTEL and we might help with it. But we're a tiny team. We don't have time to go and do all of that work. So OpenLEmetry, like interesting project. But I suspect eventually most of those semantic like that instrumentation of the big of the SDKs will live, like I say, inside the main OpenTelemetry report. I suppose. What happens to the agent frameworks? What data you basically need at the framework level to get the context is kind of unclear. I don't think we know the answer yet. But I mean, I was on the, I guess this is kind of semi-public, because I was on the call with the OpenTelemetry call last week talking about GenAI. And there was someone from Arize talking about the challenges they have trying to get OpenTelemetry data out of Langchain, where it's not like natively implemented. And obviously they're having quite a tough time. And I was realizing, hadn't really realized this before, but how lucky we are to primarily be talking about our own agent framework, where we have the control rather than trying to go and instrument other people's.Swyx [00:41:36]: Sorry, I actually didn't know about this semantic conventions thing. It looks like, yeah, it's merged into main OTel. What should people know about this? I had never heard of it before.Samuel [00:41:45]: Yeah, I think it looks like a great start. I think there's some unknowns around how you send the messages that go back and forth, which is kind of the most important part. It's the most important thing of all. And that is moved out of attributes and into OTel events. OTel events in turn are moving from being on a span to being their own top-level API where you send data. So there's a bunch of churn still going on. I'm impressed by how fast the OTel community is moving on this project. I guess they, like everyone else, get that this is important, and it's something that people are crying out to get instrumentation off. So I'm kind of pleasantly surprised at how fast they're moving, but it makes sense.Swyx [00:42:25]: I'm just kind of browsing through the specification. I can already see that this basically bakes in whatever the previous paradigm was. So now they have genai.usage.prompt tokens and genai.usage.completion tokens. And obviously now we have reasoning tokens as well. And then only one form of sampling, which is top-p. You're basically baking in or sort of reifying things that you think are important today, but it's not a super foolproof way of doing this for the future. Yeah.Samuel [00:42:54]: I mean, that's what's neat about OTel is you can always go and send another attribute and that's fine. It's just there are a bunch that are agreed on. But I would say, you know, to come back to your previous point about whether or not we should be relying on one centralized abstraction layer, this stuff is moving so fast that if you start relying on someone else's standard, you risk basically falling behind because you're relying on someone else to keep things up to date.Swyx [00:43:14]: Or you fall behind because you've got other things going on.Samuel [00:43:17]: Yeah, yeah. That's fair. That's fair.Swyx [00:43:19]: Any other observations just about building LogFire, actually? Let's just talk about this. So you announced LogFire. I was kind of only familiar with LogFire because of your Series A announcement. I actually thought you were making a separate company. I remember some amount of confusion with you when that came out. So to be clear, it's Pydantic LogFire and the company is one company that has kind of two products, an open source thing and an observability thing, correct? Yeah. I was just kind of curious, like any learnings building LogFire? So classic question is, do you use ClickHouse? Is this like the standard persistence layer? Any learnings doing that?Samuel [00:43:54]: We don't use ClickHouse. We started building our database with ClickHouse, moved off ClickHouse onto Timescale, which is a Postgres extension to do analytical databases. Wow. And then moved off Timescale onto DataFusion. And we're basically now building, it's DataFusion, but it's kind of our own database. Bogomil is not entirely happy that we went through three databases before we chose one. I'll say that. But like, we've got to the right one in the end. I think we could have realized that Timescale wasn't right. I think ClickHouse. They both taught us a lot and we're in a great place now. But like, yeah, it's been a real journey on the database in particular.Swyx [00:44:28]: Okay. So, you know, as a database nerd, I have to like double click on this, right? So ClickHouse is supposed to be the ideal backend for anything like this. And then moving from ClickHouse to Timescale is another counterintuitive move that I didn't expect because, you know, Timescale is like an extension on top of Postgres. Not super meant for like high volume logging. But like, yeah, tell us those decisions.Samuel [00:44:50]: So at the time, ClickHouse did not have good support for JSON. I was speaking to someone yesterday and said ClickHouse doesn't have good support for JSON and got roundly stepped on because apparently it does now. So they've obviously gone and built their proper JSON support. But like back when we were trying to use it, I guess a year ago or a bit more than a year ago, everything happened to be a map and maps are a pain to try and do like looking up JSON type data. And obviously all these attributes, everything you're talking about there in terms of the GenAI stuff. You can choose to make them top level columns if you want. But the simplest thing is just to put them all into a big JSON pile. And that was a problem with ClickHouse. Also, ClickHouse had some really ugly edge cases like by default, or at least until I complained about it a lot, ClickHouse thought that two nanoseconds was longer than one second because they compared intervals just by the number, not the unit. And I complained about that a lot. And then they caused it to raise an error and just say you have to have the same unit. Then I complained a bit more. And I think as I understand it now, they have some. They convert between units. But like stuff like that, when all you're looking at is when a lot of what you're doing is comparing the duration of spans was really painful. Also things like you can't subtract two date times to get an interval. You have to use the date sub function. But like the fundamental thing is because we want our end users to write SQL, the like quality of the SQL, how easy it is to write, matters way more to us than if you're building like a platform on top where your developers are going to write the SQL. And once it's written and it's working, you don't mind too much. So I think that's like one of the fundamental differences. The other problem that I have with the ClickHouse and Impact Timescale is that like the ultimate architecture, the like snowflake architecture of binary data in object store queried with some kind of cache from nearby. They both have it, but it's closed sourced and you only get it if you go and use their hosted versions. And so even if we had got through all the problems with Timescale or ClickHouse, we would end up like, you know, they would want to be taking their 80% margin. And then we would be wanting to take that would basically leave us less space for margin. Whereas data fusion. Properly open source, all of that same tooling is open source. And for us as a team of people with a lot of Rust expertise, data fusion, which is implemented in Rust, we can literally dive into it and go and change it. So, for example, I found that there were some slowdowns in data fusion's string comparison kernel for doing like string contains. And it's just Rust code. And I could go and rewrite the string comparison kernel to be faster. Or, for example, data fusion, when we started using it, didn't have JSON support. Obviously, as I've said, it's something we can do. It's something we needed. I was able to go and implement that in a weekend using our JSON parser that we built for Pydantic Core. So it's the fact that like data fusion is like for us the perfect mixture of a toolbox to build a database with, not a database. And we can go and implement stuff on top of it in a way that like if you were trying to do that in Postgres or in ClickHouse. I mean, ClickHouse would be easier because it's C++, relatively modern C++. But like as a team of people who are not C++ experts, that's much scarier than data fusion for us.Swyx [00:47:47]: Yeah, that's a beautiful rant.Alessio [00:47:49]: That's funny. Most people don't think they have agency on these projects. They're kind of like, oh, I should use this or I should use that. They're not really like, what should I pick so that I contribute the most back to it? You know, so but I think you obviously have an open source first mindset. So that makes a lot of sense.Samuel [00:48:05]: I think if we were probably better as a startup, a better startup and faster moving and just like headlong determined to get in front of customers as fast as possible, we should have just started with ClickHouse. I hope that long term we're in a better place for having worked with data fusion. We like we're quite engaged now with the data fusion community. Andrew Lam, who maintains data fusion, is an advisor to us. We're in a really good place now. But yeah, it's definitely slowed us down relative to just like building on ClickHouse and moving as fast as we can.Swyx [00:48:34]: OK, we're about to zoom out and do Pydantic run and all the other stuff. But, you know, my last question on LogFire is really, you know, at some point you run out sort of community goodwill just because like, oh, I use Pydantic. I love Pydantic. I'm going to use LogFire. OK, then you start entering the territory of the Datadogs, the Sentrys and the honeycombs. Yeah. So where are you going to really spike here? What differentiator here?Samuel [00:48:59]: I wasn't writing code in 2001, but I'm assuming that there were people talking about like web observability and then web observability stopped being a thing, not because the web stopped being a thing, but because all observability had to do web. If you were talking to people in 2010 or 2012, they would have talked about cloud observability. Now that's not a term because all observability is cloud first. The same is going to happen to gen AI. And so whether or not you're trying to compete with Datadog or with Arise and Langsmith, you've got to do first class. You've got to do general purpose observability with first class support for AI. And as far as I know, we're the only people really trying to do that. I mean, I think Datadog is starting in that direction. And to be honest, I think Datadog is a much like scarier company to compete with than the AI specific observability platforms. Because in my opinion, and I've also heard this from lots of customers, AI specific observability where you don't see everything else going on in your app is not actually that useful. Our hope is that we can build the first general purpose observability platform with first class support for AI. And that we have this open source heritage of putting developer experience first that other companies haven't done. For all I'm a fan of Datadog and what they've done. If you search Datadog logging Python. And you just try as a like a non-observability expert to get something up and running with Datadog and Python. It's not trivial, right? That's something Sentry have done amazingly well. But like there's enormous space in most of observability to do DX better.Alessio [00:50:27]: Since you mentioned Sentry, I'm curious how you thought about licensing and all of that. Obviously, your MIT license, you don't have any rolling license like Sentry has where you can only use an open source, like the one year old version of it. Was that a hard decision?Samuel [00:50:41]: So to be clear, LogFire is co-sourced. So Pydantic and Pydantic AI are MIT licensed and like properly open source. And then LogFire for now is completely closed source. And in fact, the struggles that Sentry have had with licensing and the like weird pushback the community gives when they take something that's closed source and make it source available just meant that we just avoided that whole subject matter. I think the other way to look at it is like in terms of either headcount or revenue or dollars in the bank. The amount of open source we do as a company is we've got to be open source. We're up there with the most prolific open source companies, like I say, per head. And so we didn't feel like we were morally obligated to make LogFire open source. We have Pydantic. Pydantic is a foundational library in Python. That and now Pydantic AI are our contribution to open source. And then LogFire is like openly for profit, right? As in we're not claiming otherwise. We're not sort of trying to walk a line if it's open source. But really, we want to make it hard to deploy. So you probably want to pay us. We're trying to be straight. That it's to pay for. We could change that at some point in the future, but it's not an immediate plan.Alessio [00:51:48]: All right. So the first one I saw this new I don't know if it's like a product you're building the Pydantic that run, which is a Python browser sandbox. What was the inspiration behind that? We talk a lot about code interpreter for lamps. I'm an investor in a company called E2B, which is a code sandbox as a service for remote execution. Yeah. What's the Pydantic that run story?Samuel [00:52:09]: So Pydantic that run is again completely open source. I have no interest in making it into a product. We just needed a sandbox to be able to demo LogFire in particular, but also Pydantic AI. So it doesn't have it yet, but I'm going to add basically a proxy to OpenAI and the other models so that you can run Pydantic AI in the browser. See how it works. Tweak the prompt, et cetera, et cetera. And we'll have some kind of limit per day of what you can spend on it or like what the spend is. The other thing we wanted to b

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Přestože oba zakladatelé monitorovací a debuggovací platformy Better Stack pocházejí ze Slovenska, jejich pražský startup je mnohými odborníky považován za jednu z vůbec nejperspektivnějších českých firem. Získali už dvě obří investice v celkové výši 28 600 000 $ a míří ještě mnohem výše

Tanya Bragin and I have a wide-ranging chat about the tension of open source and commercial products, Clickhouse, aligning marketing and product, and how she manages her time.

In this episode of The Frontend Masters Podcast, Todd Gardner, co-founder of TrackJS and RequestMetrics, discusses his journey from consultant to entrepreneur. He shares insights on bootstrapping SaaS products, competing against VC-backed companies, and the importance of charging customers for your product or service early. Todd delves into technical aspects of his products' stacks, including the use of .NET Core, Clickhouse, and HTMX. He offers advice on public speaking, teaching, and maintaining healthy co-founder relationships. The conversation covers web performance optimization, JavaScript error monitoring, and the challenges of balancing product development with marketing efforts. Todd also reflects on his career philosophy of continuous learning and adaptation in the fast-paced tech industry. Marc has captured his advice on startups in this article, originally an email to Todd in 2014: https://marcgrabanski.com/articles/your-advice-startups/ Check out Todd's Frontend Masters courses here: https://frontendmasters.com/teachers/todd-gardner/ Frontend Masters Online: Twitter: https://twitter.com/FrontendMasters LinkedIn: https://www.linkedin.com/company/frontend-masters/ Facebook: https://www.facebook.com/FrontendMasters Instagram: https://instagram.com/FrontendMasters About Us: Advance your skills with in-depth, modern front-end engineering courses — our 150+ high-quality courses and 18 curated learning paths will guide you from mid-level to senior developer! https://frontendmasters.com/?utm_source=spotify&utm_medium=home_link&utm_campaign=podcastepisode21

After two successful seasons of the Data Chaos podcast, we've decided to take a pause on any new episodes. We've absolutely loved all of the conversations we've had with our guests but ultimately have decided to focus our efforts on more technically nuanced content. That new content will be centered on ClickHouse. It's no secret that my company, Propel (which sponsors Data Chaos), is built using this lightning-fast columnar database. We created Propel as the easiest way to run ClickHouse at any scale and learned a ton along the way. We want to share these learnings with the community through our blogs and the new podcast. Whether you're a seasoned ClickHouse admin, a data enthusiast, or just curious about high-performance analytics, this podcast covers everything from basic concepts to advanced use cases. It features industry experts, real-world success stories, and best practices to help you get the most out of ClickHouse in your data stack. We hope you give it a listen! Spotify Apple

This week on The Business of Open Source, I spoke with Robert Hodges, CEO of Altinity. This is a great example of an open source company that is built on top of an open source project, ClickHouse, that they did not create and still do not have direct control over. Altinity has created and maintains other open source projects in the ClickHouse ecosystem as well, but So many things to unpack with this episode, but a couple I want to call attention to in particular. The origin story of how Altinity's founder discovered Clickhouse (he did not create it!). I love how Robert specifies that Alexander Zaitsev, one of the Altinity co-founders, discovered ClickHouse because he wasn't happy with how the database he was using scaled — and by the way, it had nothing to do with how much the database cost. Great example of an open source project winning because it provided superior value, not because it was/is free. Making product strategy decisions based on who the ideal user and the ideal customer is. Robert talked about how Altinity didn't contribute a particular high-security feature back to open source ClickHouse because while it's something that very security-conscious organizations would want, for an open source users who doesn't have major security and compliance requirements it would be confusing and create a worse user experience. Working in friendly competition with the 10 or so other companies that are building around ClickHouse, and how this is one of the unique things about working around open sourceHow Altinity's customers tend to value the four freedoms of open sourceAre you a leader of an open source company and you're struggling to prioritize your product roadmap in a way that reinforces your differentiated value… reach out. I help companies figure out the differentiated value of their product and product, where to put the line between the two, and how to use that information to prioritize your roadmap, build a sales narrative and communicate with your market. 

Aliaksandr Valialkin and Roman Khavronenko are Co-Founders of VictoriaMetrics, the open source time series database and monitoring platform built alongside their open source project, also called victoriametrics. In this episode, we discuss the limitations to Prometheus and how ClickHouse inspired the founders to build VictoriaMetrics, how open source helped them attract their early users and gain momentum, the importance of simplicity and saying no to feature requests that would complicate the product, their approach to an Open Core model, their unique view on funding and why bootstrapping has been an advantage for them and more!

Summary This episode features an insightful conversation with Petr Janda, the CEO and founder of Synq. Petr shares his journey from being an engineer to founding Synq, emphasizing the importance of treating data systems with the same rigor as engineering systems. He discusses the challenges and solutions in data reliability, including the need for transparency and ownership in data systems. Synq's platform helps data teams manage incidents, understand data dependencies, and ensure data quality by providing insights and automation capabilities. Petr emphasizes the need for a holistic approach to data reliability, integrating data systems into broader business processes. He highlights the role of data teams in modern organizations and how Synq is empowering them to achieve this. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Petr Janda about Synq, a data reliability platform focused on leveling up data teams by supporting a culture of engineering rigor Interview Introduction How did you get involved in the area of data management? Can you describe what Synq is and the story behind it? Data observability/reliability is a category that grew rapidly over the past ~5 years and has several vendors focused on different elements of the problem. What are the capabilities that you saw as lacking in the ecosystem which you are looking to address? Operational/infrastructure engineers have spent the past decade honing their approach to incident management and uptime commitments. How do those concepts map to the responsibilities and workflows of data teams? Tooling only plays a small part in SLAs and incident management. How does Synq help to support the cultural transformation that is necessary? What does an on-call rotation for a data engineer/data platform engineer look like as compared with an application-focused team? How does the focus on data assets/data products shift your approach to observability as compared to a table/pipeline centric approach? With the focus on sharing ownership beyond the boundaries on the data team there is a strong correlation with data governance principles. How do you see organizations incorporating Synq into their approach to data governance/compliance? Can you describe how Synq is designed/implemented? How have the scope and goals of the product changed since you first started working on it? For a team who is onboarding onto Synq, what are the steps required to get it integrated into their technology stack and workflows? What are the types of incidents/errors that you are able to identify and alert on? What does a typical incident/error resolution process look like with Synq? What are the most interesting, innovative, or unexpected ways that you have seen Synq used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Synq? When is Synq the wrong choice? What do you have planned for the future of Synq? Contact Info LinkedIn (https://www.linkedin.com/in/petr-janda/?originalSubdomain=dk) Substack (https://substack.com/@petrjanda) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com) with your story. Links Synq (https://www.synq.io/) Incident Management (https://www.pagerduty.com/resources/learn/what-is-incident-management/) SLA == Service Level Agreement (https://en.wikipedia.org/wiki/Service-level_agreement) Data Governance (https://en.wikipedia.org/wiki/Data_governance) Podcast Episode (https://www.dataengineeringpodcast.com/nicola-askham-practical-data-governance-episode-428) PagerDuty (https://www.pagerduty.com/) OpsGenie (https://www.atlassian.com/software/opsgenie) Clickhouse (https://clickhouse.com/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) dbt (https://www.getdbt.com/) Podcast Episode (https://www.dataengineeringpodcast.com/dbt-data-analytics-episode-81/) SQLMesh (https://sqlmesh.readthedocs.io/en/stable/) Podcast Episode (https://www.dataengineeringpodcast.com/sqlmesh-open-source-dataops-episode-380) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

In this episode, we sat down with Aaron Katz, the CEO of ClickHouse, a company that went from an open-source analytical database into a highly successful cloud service, utilized by Spotify, Netflix, Disney, and many more. Aaron Katz provides intriguing insights into the challenges of transitioning an open-source project into a thriving business, ClickHouse's go-to-market strategy, the role of technical support in pre-sales, and the strategic decision to avoid traditional SDR and CSM roles. CLICKHOUSE Website - https://clickhouse.com/ Twitter - https://x.com/clickhousedb Aaron Katz LinkedIn - https://www.linkedin.com/in/aaron-katz-5762094 Twitter - https://x.com/ceo_clickhouse FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro(00:56) What is ClickHouse?(04:28) What are the use cases for ClickHouse?(06:17) Reducing the latency: why the world shifts to real-time(09:05) How did ClickHouse evolve from an open-source to a cloud product?(15:01) "Open source is the future of software"(17:27) Self-hosted deployments(18:45) ClickHouse's roadmap(20:51) Is there a real-time data stack?(22:25) ClickHouse partners in data ingestion(24:32) Who are ClickHouse's main competitors?(27:35) ClickHouse's sales process(36:44) Is partnerships a good go-to-market strategy?(37:44) When is the right time for startups to start partnering?(38:22) Aaron's story of becoming the CEO(43:50) Team and culture when working on two continents(46:15) What's next?

Mike Volpi is a longtime venture capitalist who joined Index Ventures in 2009 to establish the firm's San Francisco office and North American operations. Prior, he was Chief Strategy Officer at Cisco, overseeing a run of acquisitions still studied today as a model for technology merger strategy. Mike invests primarily in artificial intelligence, infrastructure, and open-source companies, and currently serves on the boards of multiple companies including Aurora, ClickHouse, Cockroach Labs, Cohere, Confluent, Covariant.ai, Kong, Scale, Sonos, and Wealthfront.     In this episode, Kevin and Mike discuss Mike's early childhood, how he got interested in the study of engineering, and his career experiences—including what led to Mike's long career at Cisco and his current Partner position at Index—including his board experiences with multiple companies.     Mike Volpi | Index Ventures  Kevin Scott    Behind the Tech with Kevin Scott    Discover and follow other Microsoft podcasts.    

Scott and Wes dive into Cloudflare's Analytics Engine and Workers with special guest Ben Vinegar, Syntax's General Manager. Tune in as they explore Clickhouse, data tracking, infrastructure costs, and transitioning from software products to managing a podcast. Show Notes 00:00 Welcome to Syntax! 01:17 Who is Ben Vinegar? Episode 434 with Ben. 02:21 Brought to you by Sentry.io. 04:00 Cloudflare analytics engine. Counterscale.dev. Episode 634 with Armin. 09:08 What is clickhouse? 11:01 Can Clickhouse be used for things outside of analytics tracking? 13:46 What kind of events are you able to track? 15:00 How do you assign values to track? Counterscale Schema. 18:40 Data type limitations. 19:55 The troubles with sampling data. 23:57 Sample intervals. 24:24 Pricing for these services. 25:34 How it actually runs. 27:31 Infrastructure costs and pricing models. 30:19 Running production apps in Cloudflare. 31:49 Cloudflare and HonoJS. 32:47 One year with Sentry and Ben's role with Syntax. Episode 600 with David. 39:33 How does it feel going from a software project to a media project? Syntax Team. 43:00 How do you sell Syntax to Sentry? 48:37 Sick Picks & Shameless Plugs Sick Picks Ben: Randy's YouTube, Boom. Shameless Plugs Ben: Counterscale.dev Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott:X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

m3ter is revolutionizing usage-based billing for B2B software scale-ups by automating complex bill calculations and integrations, enabling companies to accelerate billing cycles, minimize revenue loss, and foster product innovation.Our platform ensures accurate, trustable billing processes, empowering businesses to focus on growth and customer success.Trusted by industry leaders like ClickHouse, Onfido, and Sift, m3ter delivers reliability, scalability, and insights, streamlining pricing and billing operations across Finance, Product, Engineering, and Sales & Customer Success teams.m3ter makes it easy to generate accurate bills you and your customers can trust.https://www.m3ter.com

Summary Maintaining a single source of truth for your data is the biggest challenge in data engineering. Different roles and tasks in the business need their own ways to access and analyze the data in the organization. In order to enable this use case, while maintaining a single point of access, the semantic layer has evolved as a technological solution to the problem. In this episode Artyom Keydunov, creator of Cube, discusses the evolution and applications of the semantic layer as a component of your data platform, and how Cube provides speed and cost optimization for your data consumers. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents data quality issues from entering every part of your data workflow, from migration to dbt deployment. Datafold has recently launched data replication testing, providing ongoing validation for source-to-target replication. Leverage Datafold's fast cross-database data diffing and Monitoring to test your replication pipelines automatically and continuously. Validate consistency between source and target at any scale, and receive alerts about any discrepancies. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold). Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Artyom Keydunov about the role of the semantic layer in your data platform Interview Introduction How did you get involved in the area of data management? Can you start by outlining the technical elements of what it means to have a "semantic layer"? In the past couple of years there was a rapid hype cycle around the "metrics layer" and "headless BI", which has largely faded. Can you give your assessment of the current state of the industry around the adoption/implementation of these concepts? What are the benefits of having a discrete service that offers the business metrics/semantic mappings as opposed to implementing those concepts as part of a more general system? (e.g. dbt, BI, warehouse marts, etc.) At what point does it become necessary/beneficial for a team to adopt such a service? What are the challenges involved in retrofitting a semantic layer into a production data system? evolution of requirements/usage patterns technical complexities/performance and cost optimization What are the most interesting, innovative, or unexpected ways that you have seen Cube used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cube? When is Cube/a semantic layer the wrong choice? What do you have planned for the future of Cube? Contact Info LinkedIn (https://www.linkedin.com/in/keydunov/) keydunov (https://github.com/keydunov) on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Cube (https://cube.dev/) Semantic Layer (https://en.wikipedia.org/wiki/Semantic_layer) Business Objects (https://en.wikipedia.org/wiki/BusinessObjects) Tableau (https://www.tableau.com/) Looker (https://cloud.google.com/looker/?hl=en) Podcast Episode (https://www.dataengineeringpodcast.com/looker-with-daniel-mintz-episode-55/) Mode (https://mode.com/) Thoughtspot (https://www.thoughtspot.com/) LightDash (https://www.lightdash.com/) Podcast Episode (https://www.dataengineeringpodcast.com/lightdash-exploratory-business-intelligence-episode-232/) Embedded Analytics (https://en.wikipedia.org/wiki/Embedded_analytics) Dimensional Modeling (https://en.wikipedia.org/wiki/Dimensional_modeling) Clickhouse (https://clickhouse.com/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) Druid (https://druid.apache.org/) BigQuery (https://cloud.google.com/bigquery?hl=en) Starburst (https://www.starburst.io/) Pinot (https://pinot.apache.org/) Snowflake (https://www.snowflake.com/en/) Podcast Episode (https://www.dataengineeringpodcast.com/snowflakedb-cloud-data-warehouse-episode-110/) Arrow Datafusion (https://arrow.apache.org/datafusion/) Metabase (https://www.metabase.com/) Podcast Episode (https://www.dataengineeringpodcast.com/metabase-with-sameer-al-sakran-episode-29) Superset (https://superset.apache.org/) Alation (https://www.alation.com/) Collibra (https://www.collibra.com/) Podcast Episode (https://www.dataengineeringpodcast.com/collibra-enterprise-data-governance-episode-188) Atlan (https://atlan.com/) Podcast Episode (https://www.dataengineeringpodcast.com/atlan-data-team-collaboration-episode-179) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary Building a database engine requires a substantial amount of engineering effort and time investment. Over the decades of research and development into building these software systems there are a number of common components that are shared across implementations. When Paul Dix decided to re-write the InfluxDB engine he found the Apache Arrow ecosystem ready and waiting with useful building blocks to accelerate the process. In this episode he explains how he used the combination of Apache Arrow, Flight, Datafusion, and Parquet to lay the foundation of the newest version of his time-series database. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit dataengineeringpodcast.com/data-council (https://www.dataengineeringpodcast.com/data-council) and use code dataengpod20 to register today! Your host is Tobias Macey and today I'm interviewing Paul Dix about his investment in the Apache Arrow ecosystem and how it led him to create the latest PFAD in database design Interview Introduction How did you get involved in the area of data management? Can you start by describing the FDAP stack and how the components combine to provide a foundational architecture for database engines? This was the core of your recent re-write of the InfluxDB engine. What were the design goals and constraints that led you to this architecture? Each of the architectural components are well engineered for their particular scope. What is the engineering work that is involved in building a cohesive platform from those components? One of the major benefits of using open source components is the network effect of ecosystem integrations. That can also be a risk when the community vision for the project doesn't align with your own goals. How have you worked to mitigate that risk in your specific platform? Can you describe the operational/architectural aspects of building a full data engine on top of the FDAP stack? What are the elements of the overall product/user experience that you had to build to create a cohesive platform? What are some of the other tools/technologies that can benefit from some or all of the pieces of the FDAP stack? What are the pieces of the Arrow ecosystem that are still immature or need further investment from the community? What are the most interesting, innovative, or unexpected ways that you have seen parts or all of the FDAP stack used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on/with the FDAP stack? When is the FDAP stack the wrong choice? What do you have planned for the future of the InfluxDB IOx engine and the FDAP stack? Contact Info LinkedIn (https://www.linkedin.com/in/pauldix/) pauldix (https://github.com/pauldix) on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links FDAP Stack Blog Post (https://www.influxdata.com/blog/flight-datafusion-arrow-parquet-fdap-architecture-influxdb/) Apache Arrow (https://arrow.apache.org/) DataFusion (https://arrow.apache.org/datafusion/) Arrow Flight (https://arrow.apache.org/docs/format/Flight.html) Apache Parquet (https://parquet.apache.org/) InfluxDB (https://www.influxdata.com/products/influxdb/) Influx Data (https://www.influxdata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/influxdb-timeseries-data-platform-episode-199) Rust Language (https://www.rust-lang.org/) DuckDB (https://duckdb.org/) ClickHouse (https://clickhouse.com/) Voltron Data (https://voltrondata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/voltron-data-apache-arrow-episode-346/) Velox (https://github.com/facebookincubator/velox) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Trino (https://trino.io/) ODBC == Open DataBase Connectivity (https://en.wikipedia.org/wiki/Open_Database_Connectivity) GeoParquet (https://github.com/opengeospatial/geoparquet) ORC == Optimized Row Columnar (https://orc.apache.org/) Avro (https://avro.apache.org/) Protocol Buffers (https://protobuf.dev/) gRPC (https://grpc.io/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Multiple open source projects and vendors have been working together to make this vision a reality. In this episode Dain Sundstrom, CTO of Starburst, explains how the combination of the Trino query engine and the Iceberg table format offer the ease of use and execution speed of data warehouses with the infinite storage and scalability of data lakes. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Join in with the event for the global data community, Data Council Austin. From March 26th-28th 2024, they'll play host to hundreds of attendees, 100 top speakers, and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working togethr to build the future of data. As a listener to the Data Engineering Podcast you can get a special discount of 20% off your ticket by using the promo code dataengpod20. Don't miss out on their only event this year! Visit: dataengineeringpodcast.com/data-council (https://www.dataengineeringpodcast.com/data-council) today. Your host is Tobias Macey and today I'm interviewing Dain Sundstrom about building a data lakehouse with Trino and Iceberg Interview Introduction How did you get involved in the area of data management? To start, can you share your definition of what constitutes a "Data Lakehouse"? What are the technical/architectural/UX challenges that have hindered the progression of lakehouses? What are the notable advancements in recent months/years that make them a more viable platform choice? There are multiple tools and vendors that have adopted the "data lakehouse" terminology. What are the benefits offered by the combination of Trino and Iceberg? What are the key points of comparison for that combination in relation to other possible selections? What are the pain points that are still prevalent in lakehouse architectures as compared to warehouse or vertically integrated systems? What progress is being made (within or across the ecosystem) to address those sharp edges? For someone who is interested in building a data lakehouse with Trino and Iceberg, how does that influence their selection of other platform elements? What are the differences in terms of pipeline design/access and usage patterns when using a Trino/Iceberg lakehouse as compared to other popular warehouse/lakehouse structures? What are the most interesting, innovative, or unexpected ways that you have seen Trino lakehouses used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the data lakehouse ecosystem? When is a lakehouse the wrong choice? What do you have planned for the future of Trino/Starburst? Contact Info LinkedIn (https://www.linkedin.com/in/dainsundstrom/) dain (https://github.com/dain) on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Trino (https://trino.io/) Starburst (https://www.starburst.io/) Presto (https://prestodb.io/) JBoss (https://en.wikipedia.org/wiki/JBoss_Enterprise_Application_Platform) Java EE (https://www.oracle.com/java/technologies/java-ee-glance.html) HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html) S3 (https://aws.amazon.com/s3/) GCS == Google Cloud Storage (https://cloud.google.com/storage?hl=en) Hive (https://hive.apache.org/) Hive ACID (https://cwiki.apache.org/confluence/display/hive/hive+transactions) Apache Ranger (https://ranger.apache.org/) OPA == Open Policy Agent (https://www.openpolicyagent.org/) Oso (https://www.osohq.com/) AWS Lakeformation (https://aws.amazon.com/lake-formation/) Tabular (https://tabular.io/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Delta Lake (https://delta.io/) Podcast Episode (https://www.dataengineeringpodcast.com/delta-lake-data-lake-episode-85/) Debezium (https://debezium.io/) Podcast Episode (https://www.dataengineeringpodcast.com/debezium-change-data-capture-episode-114) Materialized View (https://en.wikipedia.org/wiki/Materialized_view) Clickhouse (https://clickhouse.com/) Druid (https://druid.apache.org/) Hudi (https://hudi.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/hudi-streaming-data-lake-episode-209) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Robert Hodges is CEO of Altinity, which helps enterprises build real-time analytics using ClickHouse. He has been working on databases as well as the applications that use them since the early 1980s. The journey included Sybase and Oracle in the 1990s, MySQL and PostgreSQL in the early 2000s, followed by analytic databases starting in 2010. Robert's technical interests include distributed systems, open source software, and Kubernetes. He is stoked to be part of the next wave of technology based on open source analytic databases. It's a privilege to help users discover creative new uses for data and technology!

Summary Stream processing systems have long been built with a code-first design, adding SQL as a layer on top of the existing framework. RisingWave is a database engine that was created specifically for stream processing, with S3 as the storage layer. In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Your host is Tobias Macey and today I'm interviewing Yingjun Wu about the RisingWave database and the intricacies of building a stream processing engine on S3 Interview Introduction How did you get involved in the area of data management? Can you describe what RisingWave is and the story behind it? There are numerous stream processing engines, near-real-time database engines, streaming SQL systems, etc. What is the specific niche that RisingWave addresses? What are some of the platforms/architectures that teams are replacing with RisingWave? What are some of the unique capabilities/use cases that RisingWave provides over other offerings in the current ecosystem? Can you describe how RisingWave is architected and implemented? How have the design and goals/scope changed since you first started working on it? What are the core design philosophies that you rely on to prioritize the ongoing development of the project? What are the most complex engineering challenges that you have had to address in the creation of RisingWave? Can you describe a typical workflow for teams that are building on top of RisingWave? What are the user/developer experience elements that you have prioritized most highly? What are the situations where RisingWave can/should be a system of record vs. a point-in-time view of data in transit, with a data warehouse/lakehouse as the longitudinal storage and query engine? What are the most interesting, innovative, or unexpected ways that you have seen RisingWave used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on RisingWave? When is RisingWave the wrong choice? What do you have planned for the future of RisingWave? Contact Info yingjunwu (https://github.com/yingjunwu) on GitHub Personal Website (https://yingjunwu.github.io/) LinkedIn (https://www.linkedin.com/in/yingjun-wu-4b584536/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links RisingWave (https://risingwave.com/) AWS Redshift (https://aws.amazon.com/redshift/) Flink (https://flink.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/apache-flink-with-fabian-hueske-episode-57) Clickhouse (https://clickhouse.com/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) Druid (https://druid.apache.org/) Materialize (https://materialize.com/) Spark (https://spark.apache.org/) Trino (https://trino.io/) Snowflake (https://www.snowflake.com/en/) Kafka (https://kafka.apache.org/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Hudi (https://hudi.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/hudi-streaming-data-lake-episode-209) Postgres (https://www.postgresql.org/) Debezium (https://debezium.io/) Podcast Episode (https://www.dataengineeringpodcast.com/debezium-change-data-capture-episode-114) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Jamin Ball is a Partner @ Altimeter Capital where he sits on the board of Airbyte, Clickhouse, dbt Labs, Prisma, Tabular. Jamin has also led investments in Deel, MotherDuck, Personio and Starburst. Prior to Altimeter, Jamin spent 5 years at Redpoint where he led investments in Workato, Monte Carlo, Cityblock Health, Root Insurance. Ed Sim is one of the best seed round investors in venture as the Founder and Managing Partner @ Boldstart, Ed focuses specifically on developer, infra and SaaS at pre-seed and seed round. Over the last decade, Ed has backed some of the best including Snyk, BigID, Kustomer, Front and Superhuman. In Today's Episode We Discuss: 1. How to Invest Successfully in 2024: What are the three biggest mistakes growth investors can make in 2024? Why should founders not start a platform company? What were Jamin and Ed's biggest mistakes from the ZIRP era? How does Jamin justify paying an $8BN price for Hopin? What were his lessons? 2. The M&A Markets in 2024: Did Figma kill the M&A markets for 2024? What should we expect in M&A? Why will private companies buying private companies be a massive segment in 2024? What are Ed and Jamin's biggest tips to founders considering selling their company in 2024? 3. When Will IPOs Come Back: What will be the catalyst to the opening of the IPO markets? Will Stripe and Databricks go public in 2024? What others should we expect? What are the three requirements for a company to go public in 2024? 4. Firesales: Investors Need Cashback: Why does Ed believe now is the time in the cycle where late-stage investors want cash back to distribute back to their LPs or to recycle? What should we expect to see in terms of acqui-hires and firesales? What are the different incentives when comparing founders vs early stage VCs vs late stage VCs when it comes to acquisitions?

Summary The first step of data pipelines is to move the data to a place where you can process and prepare it for its eventual purpose. Data transfer systems are a critical component of data enablement, and building them to support large volumes of information is a complex endeavor. Andrei Tserakhau has dedicated his careeer to this problem, and in this episode he shares the lessons that he has learned and the work he is doing on his most recent data transfer system at DoubleCloud. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It's the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it's real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues for every part of your data workflow, from migration to deployment. Datafold has recently launched a 3-in-1 product experience to support accelerated data migrations. With Datafold, you can seamlessly plan, translate, and validate data across systems, massively accelerating your migration project. Datafold leverages cross-database diffing to compare tables across environments in seconds, column-level lineage for smarter migration planning, and a SQL translator to make moving your SQL scripts easier. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) today! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Andrei Tserakhau about operationalizing high bandwidth and low-latency change-data capture Interview Introduction How did you get involved in the area of data management? Your most recent project involves operationalizing a generalized data transfer service. What was the original problem that you were trying to solve? What were the shortcomings of other options in the ecosystem that led you to building a new system? What was the design of your initial solution to the problem? What are the sharp edges that you had to deal with to operate and use that initial implementation? What were the limitations of the system as you started to scale it? Can you describe the current architecture of your data transfer platform? What are the capabilities and constraints that you are optimizing for? As you move beyond the initial use case that started you down this path, what are the complexities involved in generalizing to add new functionality or integrate with additional platforms? What are the most interesting, innovative, or unexpected ways that you have seen your data transfer service used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the data transfer system? When is DoubleCloud Data Transfer the wrong choice? What do you have planned for the future of DoubleCloud Data Transfer? Contact Info LinkedIn (https://www.linkedin.com/in/andrei-tserakhau/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links DoubleCloud (https://double.cloud/) Kafka (https://kafka.apache.org/) MapReduce (https://en.wikipedia.org/wiki/MapReduce) Change Data Capture (https://en.wikipedia.org/wiki/Change_data_capture) Clickhouse (https://clickhouse.com/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Delta Lake (https://delta.io/) Podcast Episode (https://www.dataengineeringpodcast.com/delta-lake-data-lake-episode-85/) dbt (https://www.getdbt.com/) OpenMetadata (https://open-metadata.org/) Podcast Episode (https://www.dataengineeringpodcast.com/openmetadata-universal-metadata-layer-episode-237/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/) Speaker - Andrei Tserakhau, DoubleCloud Tech Lead. He has over 10 years of IT engineering experience and for the last 4 years was working on distributed systems with a focus on data delivery systems.