Podcasts about BigQuery

In this episode of The Marketing Factor, Austin Dandridge sits down with Julian Modiano founder of Acuto and Weavely to unpack the future of data, automation, and AI inside modern marketing agencies.Julian's rare background blends deep PPC experience from Merkle and Brainlabs with true engineering chops as a Google Cloud developer — giving him a uniquely technical and marketer-centric view of what agencies actually need. We cover data warehousing, MMM vs attribution models, AI slop, automation pitfalls, BigQuery, Looker, TikTok's rise, and whether agencies should hire developers. This episode is loaded with practical insights for performance marketers, operators, founders, and anyone building the “agency of the future.”

What does MLOps look like when you are deploying 22,000 models a month? Maddie Daianu, Head of Data and AI at Intuit Credit Karma, joins the Data Bros to pull back the curtain on one of the most high-volume data environments in FinTech. With a 100-person team serving 140 million members, standard data practices break down. Maddie shares how her team manages terabytes of daily data on Google Cloud and explains the massive strategic pivot they are undertaking right now: The move from "Information" to "Agency."

In this CRO Spotlight episode, host Warren Zenna sits down with Steven Birdsall, CRO at Alteryx, to unpack a sweeping leadership transition and how a newly formed C‑suite aligned on product and go‑to‑market. Steven shares how a product‑centric CEO and a servant‑leader CRO combine to create clarity of mandate, performance culture, and human‑first execution across sales, CS, partners, and solutions engineering.The conversation dives deep into Alteryx's evolution from workflows feeding BI to becoming the governed “canvas” for AI and agent use cases. Steven explains how business users can blend structured and unstructured data, enforce governance and access controls, and then safely bring LLMs into the same environment—pushing compute down to cloud data platforms like BigQuery, Databricks, and Snowflake.For CROs, Steven details practical AI operationalization: SDR personalization at scale, three‑dimensional agents trained on company knowledge, and revenue insights built directly on internal data. He outlines how to raise sales efficiency without scaling opex linearly, and why fast experimentation with new AI tools is now core to modern GTM orchestration.Steven closes with hiring and leadership principles for today's CRO: prioritize grit, perseverance, and customer centricity over pedigree; remove roadblocks for the field; and mentor generously. He shares how to balance data‑driven rigor with empathy, build alignment with marketing regardless of reporting lines, and stay entrepreneurial—even inside a large, complex organization.

Marie n'a jamais voulu choisir entre ambition professionnelle et soif de liberté. Alors elle a tout combiné.Diplômée de l'ESSEC, elle démarre dans le conseil en stratégie en Allemagne, convaincue que c'est sa voie. Mais rapidement, la frustration grandit : trop de politique, pas assez d'objectivité. C'est cette soif de logique qui la pousse vers la data en pleine pandémie.Pari risqué qui finit par payer. Elle décroche son premier poste de data analyst chez Papernest, devient manager en un an, puis rejoint Dougs comme première personne data avec pour mission de construire toute la stratégie from scratch. Et au moment où tout semble rouler, elle décide de tout arrêter pour partir un an faire le tour de l'Europe à vélo.——— MARIE LEFEVRE —————Retrouvez Marie sur LinkedIn : https://www.linkedin.com/in/marie-lefevre-b5770489/Articles Medium : https://medium.com/@marielefevre————— PARTIE 1/3 : PARCOURS —————(00:00) Intro + présentation(02:37) Parcours ESSEC et conseil en stratégie(06:23) Reconversion data pendant le COVID(10:41) Peurs et appréhensions dans la transition(15:22) Se sentir débordée par les demandes(19:30) C'est quoi concrètement le job de data analyst ?(26:31) Comment on fait de la data concrètement ?(35:18) Arrivée chez Dougs pour construire la data from scratch(42:05) Construire une stack data avec des compétences limitées(51:27) Comment prioriser sa liste de demandes(59:02) Définir ce que c'est la data chez Dougs(01:01:25) Recruter et faire grandir l'équipe(01:08:15) Pourquoi partir faire le tour de l'Europe à vélo(01:16:05) Ce qu'elle ramène du voyage - confiance et relativisation(01:21:00) Redéfinir son rôle au retour(01:24:22) Comment est arrivé le management(01:29:03) Erreurs en tant que manager débutant(01:35:14) Comment monter une équipe data(01:39:42) L'art de dire non dans la data(01:43:38) Évolution salariale dans la data————— PARTIE 2/3 : ROLL-BACK —————(01:51:06) Le projet complexe du calcul des primes commerciales(01:53:22) Pourquoi c'est un bourbier - exceptions et cas particuliers(01:56:04) Comment gérer cette complexité avec transparence(01:58:15) Autonomiser les équipes face aux données critiques————— PARTIE 3/3 : STAND-UP —————(01:59:19) Comment construire une architecture data fiable et robuste(02:00:53) Les outils - Airflow, Fivetran/Airbyte, BigQuery(02:03:12) DBT pour orchestrer les transformations SQL(02:08:20) Le star schema comme fondation(02:12:40) Tests et robustesse de la pipeline(02:20:39) Ressources recommandées(02:22:30) Le conseil ultime de Marie————— RESSOURCES —————Podcast Data Gen (Robin Conquet)Newsletter Data Engineering (Christophe Blefari - blef.fr)Coursera pour la formation SQL et PythonOutils : DBT, Airflow, Fivetran, Airbyte, BigQuery, Metabase, Looker Studio————— 5 ÉTOILES —————Si cet épisode vous a plu, pensez à laisser une note et un commentaire - c'est la meilleure façon de faire découvrir le podcast à d'autres personnes !Envoyez-moi une capture de cet avis (LinkedIn ou par mail à dx@donatienleon.com) et je vous enverrai une petite surprise en remerciement.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Jordan Tigani, CEO and cofounder of MotherDuck, knows what world class infrastructure looks like. He spent years building Google BigQuery before taking those lessons into the startup world. In this episode, he breaks down why building infrastructure products is fundamentally different from typical SaaS and why founders who don't understand that difference are in for a painful surprise.What You'll LearnThere are no shortcuts in infrastructure. You can't just wire together existing open source components and call it a product. Real infrastructure requires contributing meaningfully to the state of the art, and that takes time, money, and deeper technical investment than most founders expect.Starting with startups, not enterprises, is often the smarter play. Early stage infrastructure companies should target other startups first because they're more comfortable with bleeding edge tech, have lower security barriers, and won't force you to spend three engineers building custom auth instead of your actual product.Scaling down is the new scaling up. Jordan saw pressure at SingleStore to make databases smaller and more efficient, not just bigger. That insight led to MotherDuck, which is built on DuckDB—a database that can run in a car, scale to massive cloud instances, and challenge the coordination overhead of legacy distributed systems.Bottoms up engineering cultures win in infrastructure. At BigQuery, engineers close to customer problems could ship fast and independently. Jordan's recreating that at MotherDuck by removing layers between engineers and customers, because creative problem solving requires understanding business constraints, not just technical ones.Convincing people you can scale is half the battle. The best proof is customers who look like your next target and can vouch for you. Next best is real data and benchmarks. If you don't have those yet, lean on implementation support and help prospects test at scale themselves. Early on, sometimes all you have is your word.Timestamped Highlights[01:22] Why infrastructure takes longer to build than typical SaaS products and why there's no shallow way to do it[06:57] The MVP dilemma: finding product market fit when enterprises demand reliability from day one[11:44] Lessons from BigQuery and SingleStore—what to carry over from big tech and what to leave behind[21:21] The gap in the market that led to MotherDuck: why distributed databases don't scale down and why that matters now[26:10] Redefining scale: why 100 users on one giant instance isn't necessarily better than 100 auto scaling individual instances[29:08] The hierarchy of proof: from customer testimonials to benchmarks to trust me, it'll workA Line to Remember“If you really want to build an infrastructure product, you can't just string existing components together. You actually have to contribute meaningfully to improving the state of the art.”Stay ConnectedIf this breakdown of infrastructure startups resonated with you, subscribe so you don't miss future episodes. And if you're building in this space or thinking about it, connect with Jordan on LinkedIn. He's committed to paying forward the help he got as a founder.

Construire une architecture data from scratch, c'est un projet intimidant. Quels outils choisir ? Comment s'assurer que tout tient la route dans le temps ?Marie Lefevre, Lead Data Analyst chez Dougs, partage la stack data qu'elle a mise en place et qui fonctionne. Airflow, Fivetran, BigQuery, DBT, Metabase...Elle détaille chaque brique, son rôle, et pourquoi ces choix ont du sens pour une équipe de 5 personnes.Mais au-delà des outils, Marie insiste sur un point crucial : la robustesse ne vient pas que de la tech. Elle vient aussi des règles qu'on se fixe, de la discipline qu'on s'impose, et des tests qu'on met en place pour détecter les anomalies avant qu'elles ne cassent tout.————— MARIE LEFEVRE —————Retrouvez Marie sur LinkedIn : https://www.linkedin.com/in/marie-lefevre-b5770489/————— 5 ÉTOILES ————— Si cet épisode vous a plu, pensez à laisser une note et un commentaire - c'est la meilleure façon de faire découvrir le podcast à d'autres personnes !Envoyez-moi une capture de cet avis (LinkedIn ou par mail à dx@donatienleon.com) et je vous enverrai une petite surprise en remerciement.

Soham Mazumdar, CEO and Co-Founder of WisdomAI, discusses how organizations can break free from the "drowning in data but starving for insights" paradox that plagues modern enterprises. We explore his journey from Google's TeraGoogle project to co-founding and scaling Rubrik through its $5.6 billion IPO, and why he left that success to build an agentic AI approach to Business Intelligence (BI) that transforms how businesses extract value from their data investments.SHOW: 971SHOW TRANSCRIPT: The Cloudcast #963 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:WisdomAI websiteTopic 1 - Welcome to the show, Soham. We overlapped briefly at Rubrik. Give everyone a quick introduction and tell everyone a bit about your time at Google prior to RubrikTopic 2 - You helped scale Rubrik from inception to a $5.6 billion IPO in 2024. What was the "aha moment" that made you leave that success to tackle the enterprise data analytics problem with WisdomAI?Topic 3 - Let's define the core problem. Organizations invest heavily in modern data platforms - Snowflake, Databricks, etc. - but there is the term "drowning in data but starving for insights." What's broken in the traditional BI stack that prevents business users from getting answers?Topic 4 - How do agentic AI and BI fit together? WisdomAI introduces the concept of "Knowledge Fabric" and agentic data insights. Break this down for us - how does this fundamentally differ from traditional dashboards and BI tools?Topic 5 - One of the biggest challenges with GenAI in enterprise settings is hallucination. You've emphasized that WisdomAI separates GenAI from answer generation. How does your approach tackle this critical trust issue?Topic 6 - Let's talk about data integration complexity. Your platform works with both structured and unstructured data - Snowflake, BigQuery, Redshift, but also Excel, PDFs, PowerPoints. How do you handle this "dirty" data reality that most enterprises face?Topic 6a - With so much data, how do most organizations get started? What's a typical use case for adoption?Topic 7 - If anyone is interested, what's the best way to get started?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

This episode is sponsored by SearchMaster, the leader in AI Search Optimization and traditional paid search keyword optimization. Future-proof your SEO strategy. Sign up now for free!   Watch this episode on YouTube!   On this episode of the Marketing x Analytics Podcast, host Alex Sofronas talks with Joshua Lauer, CEO of Lauer Creations, about marketing intelligence consulting. Joshua discusses consolidating various marketing data sources into a data warehouse, automating reporting with tools like Google Analytics, BigQuery, and Looker Data Studio, and ensuring accurate tracking. He also covers metrics that businesses should focus on, potential pitfalls in marketing data and attribution, and the benefits of both internal and external data management resources. He concludes by offering a deep dive audit for interested listeners. Follow Marketing x Analytics! X          |          LinkedIn Click Here for Transcribed Episodes of Marketing x Analytics All view are our own.

Parce que… c'est l'épisode 0x650! Shameless plug 4 et 5 novembre 2025 - FAIRCON 2025 8 et 9 novembre 2025 - DEATHcon 17 au 20 novembre 2025 - European Cyber Week 25 et 26 février 2026 - SéQCure 2026 Description Introduction Dans cet épisode du podcast Police Secure, Clément Cruchet présente une analyse approfondie de la surface d'attaque de Google Cloud Platform (GCP), un sujet souvent négligé dans la communauté de la cybersécurité. Contrairement à Azure et AWS qui bénéficient d'une documentation abondante sur leurs vulnérabilités et vecteurs d'attaque, GCP reste le “petit frère oublié” du cloud computing. Cette présentation, donnée lors de la conférence Bide, vise à combler cette lacune en explorant les chemins qu'un attaquant pourrait emprunter dans un environnement GCP. Le contexte : pourquoi GCP est moins documenté Clément observe qu'il y a trois ou quatre ans, la documentation sur les vulnérabilités GCP était quasi inexistante. Cette absence de contenu a même conduit certains utilisateurs sur des forums comme Reddit à affirmer de manière erronée que GCP était plus sûr ou exempt de mauvaises configurations. En réalité, ces failles existent bel et bien, mais elles n'avaient simplement pas été explorées en profondeur. Bien que la situation se soit améliorée depuis trois ans avec l'apparition de formations et de certifications, GCP demeure significativement moins couvert que ses concurrents. L'importance de l'IAM (Identity and Access Management) Le cœur de la sécurité dans tous les environnements cloud réside dans la gestion des identités et des accès. Que ce soit Azure, AWS, GCP ou d'autres fournisseurs comme Oracle Cloud ou Alibaba Cloud, chacun possède son propre modèle IAM distinct. Ces modèles constituent la base de toute gestion des permissions, rôles et autorisations dans les environnements cloud. Le paradoxe est clair : sans permissions IAM, on ne peut rien faire, mais avec trop de permissions, on ouvre la porte à des abus et des défauts de configuration. La majorité des vulnérabilités dans les environnements cloud proviennent justement de ces mauvaises configurations au sein de l'IAM. La hiérarchie unique de GCP GCP se distingue par sa structure hiérarchique particulière. Contrairement à AWS qui fonctionne avec des comptes, ou à Azure qui utilise des tenants, des subscriptions et des groupes de ressources, GCP adopte une approche top-down très structurée. Au sommet se trouve l'organisation, généralement liée au nom de domaine de l'entreprise (par exemple company.com). Sous l'organisation, on trouve des folders, comparables aux unités organisationnelles (OU) d'Active Directory. Ces folders contiennent ensuite des projets, qui constituent l'unité administrative la plus importante. Les projets dans GCP peuvent être comparés aux comptes AWS et c'est principalement à ce niveau que se fait la facturation. Pour beaucoup d'utilisateurs, seule la vue du projet est accessible, sans nécessairement avoir besoin d'une organisation complète. Cette flexibilité permet de commencer à travailler directement avec un projet sans passer par la création d'une infrastructure organisationnelle complète. Les rôles et leurs dangers Un point crucial soulevé par Clément concerne les rôles primitifs dans GCP : éditeur, viewer, owner et browser. Ces rôles sont extrêmement dangereux car ils accordent des permissions bien trop larges. Par exemple, un rôle d'éditeur peut avoir accès à 800 permissions différentes, ce qui viole complètement le principe du moindre privilège. Le message clé est de ne jamais utiliser ces rôles primitifs dans une infrastructure GCP. Même les rôles prédéfinis, pourtant plus granulaires, peuvent présenter des risques. Un rôle comme “compute admin”, qui devrait théoriquement se limiter à l'administration des ressources compute, peut en réalité inclure 800 permissions, dont certaines touchent à des services non liés comme BigQuery. La recommandation fondamentale est de créer des rôles personnalisés aussi granulaires que possible et d'appliquer systématiquement le principe du moindre privilège. Domain wide delegation : un vecteur d'exfiltration méconnu L'une des contributions majeures de cette présentation concerne le domain wide delegation, une technique d'exfiltration peu documentée. Cette fonctionnalité permet à un compte de service dans GCP d'interagir avec Google Workspace : accéder à Drive, Gmail, envoyer des emails au nom d'utilisateurs, récupérer des pièces jointes, etc. Clément a développé un outil Python appelé “Delegate” pour démontrer et tester cette technique. Lorsqu'il a écrit son article de blog sur le sujet début 2023, il n'existait pratiquement aucune documentation sur cette vulnérabilité. Ironiquement, Palo Alto Networks a publié un article similaire plusieurs mois après, ce qui témoigne du caractère précurseur de ses recherches. Le scénario d'attaque typique implique un attaquant qui compromet une machine virtuelle possédant un compte de service capable d'effectuer du domain wide delegation. Cette technique peut également servir de mécanisme de persistance, permettant à un attaquant de configurer sa propre délégation pour exfiltrer des données de manière discrète. L'outil Delegate permet de lire des emails, télécharger et uploader des fichiers sur Drive, offrant ainsi une capacité d'exfiltration complète. La matrice d'attaque GCP Pour synthétiser ses recherches, Clément propose une kill chain communautaire spécifique à GCP, disponible sur GitHub (github.com/otendfreed/GCP-attack-matrix). Cette matrice d'attaque représente l'ensemble des tactiques, techniques et procédures (TTP) depuis la reconnaissance jusqu'à l'exfiltration et l'impact. L'objectif est de fournir un outil pour les équipes de sécurité souhaitant effectuer du purple teaming dans des environnements GCP, leur permettant d'évaluer leurs contrôles de sécurité et leur capacité de détection. Conclusion Ce podcast souligne l'importance de ne pas négliger GCP dans les stratégies de sécurité cloud. Bien que moins documenté, ce fournisseur présente des vecteurs d'attaque tout aussi critiques que ses concurrents. La recherche communautaire et le partage de connaissances sont essentiels pour identifier et corriger les vulnérabilités avant que des attaquants malveillants ne les exploitent. Comme le souligne Clément, pour attaquer un système, il faut d'abord le comprendre, et c'est précisément cette compréhension qu'il cherche à transmettre à la communauté de la cybersécurité. Notes À venir Collaborateurs Nicolas-Loïc Fortin Clément Cruchet Crédits Montage par Intrasecure inc Locaux réels par Bsides Montréal

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 :

Full show notes, transcript and AI chatbot - http://bit.ly/47craLCWatch on YouTube - https://youtu.be/lvzrZTee9lY-----Episode Summary:In this episode of The Measure Pod, Dara and Matthew sit down with Johan van de Werken from GA4Dataform to talk about the evolving world of analytics engineering and data transformation. From his early career journey to his work with BigQuery and large language models, Johan shares insights into building scalable data workflows and the growing demand for learning platforms in analytics. They discuss the origins of Dataform, its connection with dbt, and how it's enabling analysts to do more with GA4 data. Along the way, they touch on community insights from Superweek and explore how tools like GA4Dataform are reshaping the way teams think about data modeling and automation.-----About The Measure Pod:The Measure Pod is your go-to fortnightly podcast hosted by seasoned analytics pros. Join Dara Fitzgerald (Co-Founder at Measurelab) & Matthew Hooson (Head of Engineering at Measurelab) as they dive into the world of data, analytics and measurement, with a side of fun.-----If you liked this episode, don't forget to subscribe to The Measure Pod on your favourite podcast platform and leave us a review. Let's make sense of the analytics industry together!

Welcome to AI Unraveled, your daily briefing on the real world business impact of AI.Are you preparing for the challenging Google Cloud Professional Machine Learning Engineer certification? This episode is your secret weapon! In less than 18 minutes, we deliver a rapid-fire guided study session packed with 10 exam-style practice questions and actionable "study hacks" to lock in the key concepts.We cut through the complexity of Google's powerful AI services, focusing on core topics like MLOps with Vertex AI, large-scale data processing with Dataflow, and feature engineering in BigQuery. This isn't just a Q&A; it's a focused training session designed to help you think like a certified Google Cloud ML expert and ace your exam.In This Episode, You'll Learn:ML Problem Framing: How to instantly tell the difference between a regression and a classification problem.Data Preprocessing: When to use Dataflow for unstructured data vs. BigQuery for structured data.Feature Engineering: The best practice for handling high-cardinality categorical features in a neural network.Vertex AI Training: The critical decision point between using a pre-built or a custom training container.Hyperparameter Tuning: How to use Vertex AI Vizier efficiently when you're on a limited budget.Model Deployment: The key differences between online and batch prediction for real-world applications.MLOps Automation: How to orchestrate a complete, reproducible workflow with Vertex AI Pipelines.Model Monitoring: How to spot and diagnose training-serving skew to maintain model performance.Responsible AI: Using the What-If Tool to investigate model fairness and mitigate bias.Serverless Architecture: A simple, powerful pattern for building event-driven ML systems with Cloud Functions.

Breaking: Google just released Gemini Enterprise.

This Podcast is sponsored by Team Simmer.Go to TeamSimmer and use the coupon code DEVIATE for 10% on individual course purchases.The Technical Marketing Handbook provides a comprehensive journey through technical marketing principles.Sign up to the Simmer Newsletter for the latest news in Technical Marketing.NEW! - Mastering GA4 With Google BigQuery Course with Johan van de Werken is now out and you can get 15% discount on it if you buy it by the end of the month (September). The 15% discount will be applied automatically at checkout! Doesn't work together with another discount code. Get it here: https://www.teamsimmer.com/all-courses/mastering-ga4-with-google-bigquery/Latest content from Juliana & Simo:Subscribe to Juliana's newsletter: https://julianajackson.substack.com/Latest on the SimoAhava.com blog > #GTMTips: How To Load Google Scripts From A Server Container - https://www.simoahava.com/gtmtips/new-way-load-google-scripts-server-container/Latest from Juliana: https://julianajackson.substack.com/p/how-to-do-data-analysisAlso mentioned in the episode:Loads of goodies on sGTM Pantheon from Gunnar Griese: https://gunnargriese.com/tags/gtm-server-side/GA4 Dataform - https://ga4dataform.com/ (shouts to Jules, Krisztián, Johan, Artem, Simon)Analytics Summit - https://www.analytics-summit.com/Measure Summit - https://measuresummit.com/Measurecamp Helsinki - https://helsinki.measurecamp.org/Google Tag Gateway - https://developers.google.com/tag-platform/tag-manager/gateway/setup-guide?setup=manualsGTM Pantheon - https://github.com/google-marketing-solutions/gps-sgtm-pantheonArben Kqiku - upcoming instructor on Team Simmer for R for Data analysis - https://www.linkedin.com/in/arben-kqiku-301457117/ This podcast is brought to you by Juliana Jackson and Simo Ahava.

Jean-Benoît Mehaut et Mustapha Benosmane sont Global Head of Analytics et Head of Data Platform du groupe Adeo, le leader européen du bricolage qui rassemble notamment Leroy Merlin, Bricoman, Saint-Maclou et Weldom dans 11 pays avec 115 000 collaborateurs. Depuis 4 ans, ils ont piloté la mise en place d'une approche Data Mesh à l'échelle dans 11 pays pour 2000 utilisateurs internes afin de rendre la donnée plus qualitative et actionnable pour les métiers.On aborde :

Join Shane Gibson and Nigel Vining as they describe and discuss the AgileData Engineering Pattern for the Data Match.   The Data Match pattern provides an automated, granular comparison capability to efficiently identify and report discrepancies between two datasets, moving from row counts to specific data values. This 'data diff' solution transforms hours of manual data reconciliation into minutes by optimising comparisons for cloud analytics databases like BigQuery, serving as a support feature for on-demand exception handling rather than a continuous trust rule.   An AgileData Engineering Pattern is a repeatable, proven approach for solving a common data engineering challenge in a simple, consistent, and scalable way, designed to reduce rework, speed up delivery, and embed quality by default.   If you want a copy of the pattern template head over to: https://agiledata.substack.com/i/172820886/pattern-name Discover more  AgileData Engineering Patterns over at https://agiledata.substack.com/s/agiledata-engineering-patterns   If you just want to talk about making magic happen with agile and data you can connect with Shane @shagility on LinkedIn.   Subscribe: Apple Podcast | Spotify | Google Podcast  | Amazon Audible | TuneIn | iHeartRadio | PlayerFM | Listen Notes | Podchaser |  Deezer | Podcast Addict |  Buy the Green Book now!   Simply Magical Data Ways of Working

Marketing is changing forever.    In this episode of Eye on AI, host Craig Smith sits down with Chris O'Neill, CEO of GrowthLoop and board member at Gap, to explore how agentic AI and GrowthLoop's Compound Marketing Engine are transforming the way brands connect with their customers.   Chris shares how GrowthLoop applies AI on top of modern data clouds like Snowflake, BigQuery, and Databricks to automate audience targeting, personalize campaigns in real time, and accelerate experimentation loops.    He explains why speed and iteration matter more than ever, how companies like Allegro doubled their return on ad spend with GrowthLoop, and why the future of marketing belongs to brands that embrace agentic AI.   If you're a marketer, technologist, or business leader looking to stay ahead in the age of AI, this conversation is packed with practical insights you can't afford to miss. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI

Mes 2 pires angoisses de CTO :

Send us a textIn this episode, Frank and SteveO cover the latest cloud updates that matter for FinOps practitioners:Compute & AI: AWS launches the P6e GB200 Ultra Servers, delivering record-breaking GPU performance for training and inference at trillion-parameter scale. Google announces FlexStart VMs to lower inference costs, while Azure rolls out free AWS-to-Azure Blob migration.Storage & Data: Google introduces editable backup plans, and AWS adds tagging support for S3 Express One Zone—a step toward using tags as operational levers, not just reporting tools.Visibility & Optimization: AWS Transform enhances EBS cost analysis and .NET modernization insights. GCP improves billing exports with spend-based CUD metadata in BigQuery and previews a Cost Explorer for better spend tracking.Pricing & Commitments: AWS Connect introduces per-day pricing for external voice connectors. Google expands flexible CUDs to cover Cloud Run services, with full migration to the new model coming in January 2026.Savings & Compliance: Azure Firewall adds ingestion-time log transformations to cut monitoring costs. AWS Audit Manager improves evidence collection, reducing compliance overhead and spend.AI-assisted Operations: AWS debuts MCP servers for S3 Tables, CloudWatch, and Application Signals—enabling AI-driven data access, troubleshooting, and observability. Plus, QuickSight doubles SPICE datasets to 2B rows.As always, we cut through the noise to focus on the FinOps impact—cost, commitments, compliance, and the growing role of AI in managing the cloud.

Fredrik talks to Matt Topol about Arrow and how the Arrow ecosystem is evolving. Arrow is an open source, columnar in-memory data format designed for efficient data processing and analytics - which means passing data between things without needing to transform it, and ideally even without needing to copy it. What makes the ecosystem grow, and why is it very cool to have Arrow on the GPU? What is the connection between Arrow, machine learning, and Hugging face? Matt emphasizes the value of open standards, even as they work with or within more closed systems they can help open things up, and help bring about more modular solutions so that developers can focus on doing their core area really well. This episode can be seen as a follow-up to episode 567, where Matt first joined to discuss everything Arrow. Recorded during Øredev 2024. Thank you Cloudnet for sponsoring our VPS! Comments, questions or tips? We a re @kodsnack, @tobiashieta, @oferlund and @bjoreman on Twitter, have a page on Facebook and can be emailed at info@kodsnack.se if you want to write longer. We read everything we receive. If you enjoy Kodsnack we would love a review in iTunes! You can also support the podcast by buying us a coffee (or two!) through Ko-fi. Links Matt Matt’s Øredev 2023 talks: State of the Apache Arrow ecosystem: How your project can leverage Arrow! and Leveraging Apache Arrow for ML workflows Previous episodes with Matt Øredev 2024 Matt’s Øredev 2024 talks - on Arrow ADBC and Composable and modular data systems ADBC - Arrow database connectivity Arrow Snowflake Snowflake drivers for ADBC Bigquery The Bigquery driver Microsoft Fabric Duckdb Postgres SQLite Arrow flight - RPC framework for services based on Arrow data Arrow flight SQL Microsoft Power BI Velox Apache datafusion Query planning Substrait - query IR Polaris Libcudf Nvidia RAPIDS Pytorch Tensorflow Arrow device interface DLPack - in-memory tensor structure Tensors Nanoarrow Voltron data - where Matt used to work. He’s now at Columnar Theseus GPU compute engine The composable data management system manifesto Support us on Ko-fi! Matt’s book - In-memory analytics with Apache Arrow Spark Spark connect RPC UDFs Photon Datafusion Apache Cassandra ODBC JDBC R - programming language for statistical computing Hugging face Ray Stringview - “German-style strings” Scaling up with R and Arrow - the book on using Arrow with R Titles It’s gotten a lot bigger The bones of it are in the repo (Powered by ADBC) Individual compute components Feed it substrate Where the ecosystem is going Arrow on the GPU The data stays on the GPU A forced copy Leverage that device interface Without forcing the copy Shy of that last mile Turtles all the way down The guy who said yes German-style strings

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the pitfalls and best practices of “vibe coding” with generative AI. You will discover why merely letting AI write code creates significant risks. You will learn essential strategies for defining robust requirements and implementing critical testing. You will understand how to integrate security measures and quality checks into your AI-driven projects. You will gain insights into the critical human expertise needed to build stable and secure applications with AI. Tune in to learn how to master responsible AI coding and avoid common mistakes! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast_everything_wrong_with_vibe_coding_and_how_to_fix_it.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn – 00:00 In this week’s In-Ear Insights, if you go on LinkedIn, everybody, including tons of non-coding folks, has jumped into vibe coding, the term coined by OpenAI co-founder Andre Karpathy. A lot of people are doing some really cool stuff with it. However, a lot of people are also, as you can see on X in a variety of posts, finding out the hard way that if you don’t know what to ask for—say, application security—bad things can happen. Katie, how are you doing with giving into the vibes? Katie Robbert – 00:38 I’m not. I’ve talked about this on other episodes before. For those who don’t know, I have an extensive background in managing software development. I myself am not a software developer, but I have spent enough time building and managing those teams that I know what to look for and where things can go wrong. I’m still really skeptical of vibe coding. We talked about this on a previous podcast, which if you want to find our podcast, it’s @TrustInsightsAI_TIpodcast, or you can watch it on YouTube. My concern, my criticism, my skepticism of vibe coding is if you don’t have the basic foundation of the SDLC, the software development lifecycle, then it’s very easy for you to not do vibe coding correctly. Katie Robbert – 01:42 My understanding is vibe coding is you’re supposed to let the machine do it. I think that’s a complete misunderstanding of what’s actually happening because you still have to give the machine instruction and guardrails. The machine is creating AI. Generative AI is creating the actual code. It’s putting together the pieces—the commands that comprise a set of JSON code or Python code or whatever it is you’re saying, “I want to create an app that does this.” And generative AI is like, “Cool, let’s do it.” You’re going through the steps. You still need to know what you’re doing. That’s my concern. Chris, you have recently been working on a few things, and I’m curious to hear, because I know you rely on generative AI because yourself, you’ve said, are not a developer. What are some things that you’ve run into? Katie Robbert – 02:42 What are some lessons that you’ve learned along the way as you’ve been vibing? Christopher S. Penn – 02:50 Process is the foundation of good vibe coding, of knowing what to ask for. Think about it this way. If you were to say to Claude, ChatGPT, or Gemini, “Hey, write me a fiction novel set in the 1850s that’s a drama,” what are you going to get? You’re going to get something that’s not very good. Because you didn’t provide enough information. You just said, “Let’s do the thing.” You’re leaving everything up to the machine. That prompt—just that prompt alone. If you think about an app like a book, in this example, it’s going to be slop. It’s not going to be very good. It’s not going to be very detailed. Christopher S. Penn – 03:28 Granted, it doesn’t have the issues of code, but it’s going to suck. If, on the other hand, you said, “Hey, here’s the ideas I had for all the characters, here’s the ideas I had for the plot, here’s the ideas I had for the setting. But I want to have these twists. Here’s the ideas for the readability and the language I want you to use.” You provided it with lots and lots of information. You’re going to get a better result. You’re going to get something—a book that’s worth reading—because it’s got your ideas in it, it’s got your level of detail in it. That’s how you would write a book. The same thing is true of coding. You need to have, “Here’s the architecture, here’s the security requirements,” which is a big, big gap. Christopher S. Penn – 04:09 Here’s how to do unit testing, here’s the fact why unit tests are important. I hated when I was writing code by myself, I hated testing. I always thought, Oh my God, this is the worst thing in the world to have to test everything. With generative AI coding tools, I now am in love with testing because, in fact, I now follow what’s called test-driven development, where you write the tests first before you even write the production code. Because I don’t have to do it. I can say, “Here’s the code, here’s the ideas, here’s the questions I have, here’s the requirements for security, here’s the standards I want you to use.” I’ve written all that out, machine. “You go do this and run these tests until they’re clean, and you’ll just keep running over and fix those problems.” Christopher S. Penn – 04:54 After every cycle you do it, but it has to be free of errors before you can move on. The tools are very capable of doing that. Katie Robbert – 05:03 You didn’t answer my question, though. Christopher S. Penn – 05:05 Okay. Katie Robbert – 05:06 My question to you was, Chris Penn, what lessons have you specifically learned about going through this? What’s been going on, as much as you can share, because obviously we’re under NDA. What have you learned? Christopher S. Penn – 05:23 What I’ve learned: documentation and code drift very quickly. You have your PRD, you have your requirements document, you have your work plans. Then, as time goes on and you’re making fixes to things, the code and the documentation get out of sync very quickly. I’ll show an example of this. I’ll describe what we’re seeing because it’s just a static screenshot, but in the new Claude code, you have the ability to build agents. These are built-in mini-apps. My first one there, Document Code Drift Auditor, goes through and says, “Hey, here’s where your documentation is out of line with the reality of your code,” which is a big deal to make sure that things stay in sync. Christopher S. Penn – 06:11 The second one is a Code Quality Auditor. One of the big lessons is you can’t just say, “Fix my code.” You have to say, “You need to give me an audit of what’s good about my code, what’s bad about my code, what’s missing from my code, what’s unnecessary from my code, and what silent errors are there.” Because that’s a big one that I’ve had trouble with is silent errors where there’s not something obviously broken, but it’s not quite doing what you want. These tools can find that. I can’t as a person. That’s just me. Because I can’t see what’s not there. A third one, Code Base Standards Inspector, to look at the standards. This is one that it says, “Here’s a checklist” because I had to write—I had to learn to write—a checklist of. Christopher S. Penn – 06:51 These are the individual things I need you to find that I’ve done or not done in the codebase. The fourth one is logging. I used to hate logging. Now I love logs because I can say in the PRD, in the requirements document, up front and throughout the application, “Write detailed logs about what’s happening with my application” because that helps machine debug faster. I used to hate logs, and now I love them. I have an agent here that says, “Go read the logs, find errors, fix them.” Fifth lesson: debt collection. Technical debt is a big issue. This is when stuff just accumulates. As clients have new requests, “Oh, we want to do this and this and this.” Your code starts to drift even from its original incarnation. Christopher S. Penn – 07:40 These tools don’t know to clean that up unless you tell it to. I have a debt collector agent that goes through and says, “Hey, this is a bunch of stuff that has no purpose anymore.” And we can then have a conversation about getting rid of it without breaking things. Which, as a thing, the next two are painful lessons that I’ve learned. Progress Logger essentially says, after every set of changes, you need to write a detailed log file in this folder of that change and what you did. The last one is called Docs as Data Curator. Christopher S. Penn – 08:15 This is where the tool goes through and it creates metadata at the top of every progress entry that says, “Here’s the keywords about what this bug fixes” so that I can later go back and say, “Show me all the bug fixes that we’ve done for BigQuery or SQLite or this or that or the other thing.” Because what I found the hard way was the tools can introduce regressions. They can go back and keep making the same mistake over and over again if they don’t have a logbook of, “Here’s what I did and what happened, whether it worked or not.” By having these set—these seven tools, these eight tools—in place, I can prevent a lot of those behaviors that generative AI tends to have. Christopher S. Penn – 08:54 In the same way that you provide a writing style guide so that AI doesn’t keep making the mistake of using em dashes or saying, “in a world of,” or whatever the things that you do in writing. My hard-earned lessons I’ve encoded into agents now so that I don’t keep making those mistakes, and AI doesn’t keep making those mistakes. Katie Robbert – 09:17 I feel you’re demonstrating my point of my skepticism with vibe coding because you just described a very lengthy process and a lot of learnings. I’m assuming what was probably a lot of research up front on software development best practices. I actually remember the day that you were introduced to unit tests. It wasn’t that long ago. And you’re like, “Oh, well, this makes it a lot easier.” Those are the kinds of things that, because, admittedly, software development is not your trade, it’s not your skillset. Those are things that you wouldn’t necessarily know unless you were a software developer. Katie Robbert – 10:00 This is my skepticism of vibe coding: sure, anybody can use generative AI to write some code and put together an app, but then how stable is it, how secure is it? You still have to know what you’re doing. I think that—not to be too skeptical, but I am—the more accessible generative AI becomes, the more fragile software development is going to become. It’s one thing to write a blog post; there’s not a whole lot of structure there. It’s not powering your website, it’s not the infrastructure that holds together your entire business, but code is. Katie Robbert – 11:03 That’s where I get really uncomfortable. I’m fine with using generative AI if you know what you’re doing. I have enough knowledge that I could use generative AI for software development. It’s still going to be flawed, it’s still going to have issues. Even the most experienced software developer doesn’t get it right the first time. I’ve never in my entire career seen that happen. There is no such thing as the perfect set of code the first time. I think that people who are inexperienced with the software development lifecycle aren’t going to know about unit tests, aren’t going to know about test-based coding, or peer testing, or even just basic QA. Katie Robbert – 11:57 It’s not just, “Did it do the thing,” but it’s also, “Did it do the thing on different operating systems, on different browsers, in different environments, with people doing things you didn’t ask them to do, but suddenly they break things?” Because even though you put the big “push me” button right here, someone’s still going to try to click over here and then say, “I clicked on your logo. It didn’t work.” Christopher S. Penn – 12:21 Even the vocabulary is an issue. I’ll give you four words that would automatically uplevel your Python vibe coding better. But these are four words that you probably have never heard of: Ruff, MyPy, Pytest, Bandit. Those are four automated testing utilities that exist in the Python ecosystem. They’ve been free forever. Ruff cleans up and does linting. It says, “Hey, you screwed this up. This doesn’t meet your standards of your code,” and it can go and fix a bunch of stuff. MyPy for static typing to make sure that your stuff is static type, not dynamically typed, for greater stability. Pytest runs your unit tests, of course. Bandit looks for security holes in your Python code. Christopher S. Penn – 13:09 If you don’t know those exist, you probably say you’re a marketer who’s doing vibe coding for the first time, because you don’t know they exist. They are not accessible to you, and generative AI will not tell you they exist. Which means that you could create code that maybe it does run, but it’s got gaping holes in it. When I look at my standards, I have a document of coding standards that I’ve developed because of all the mistakes I’ve made that it now goes in every project. This goes, “Boom, drop it in,” and those are part of the requirements. This is again going back to the book example. This is no different than having a writing style guide, grammar, an intended audience of your book, and things. Christopher S. Penn – 13:57 The same things that you would go through to be a good author using generative AI, you have to do for coding. There’s more specific technical language. But I would be very concerned if anyone, coder or non-coder, was just releasing stuff that didn’t have the right safeguards in it and didn’t have good enough testing and evaluation. Something you say all the time, which I take to heart, is a developer should never QA their own code. Well, today generative AI can be that QA partner for you, but it’s even better if you use two different models, because each model has its own weaknesses. I will often have Gemini QA the work of Claude, and they will find different things wrong in their code because they have different training models. These two tools can work together to say, “What about this?” Christopher S. Penn – 14:48 “What about this?” And they will. I’ve actually seen them argue, “The previous developers said this. That’s not true,” which is entertaining. But even just knowing that rule exists—a developer should not QA their own code—is a blind spot that your average vibe coder is not going to have. Katie Robbert – 15:04 Something I want to go back to that you were touching upon was the privacy. I’ve seen a lot of people put together an app that collects information. It could collect basic contact information, it could collect other kind of demographic information, it can collect opinions and thoughts, or somehow it’s collecting some kind of information. This is also a huge risk area. Data privacy has always been a risk. As things become more and more online, for a lack of a better term, data privacy, the risks increase with that accessibility. Katie Robbert – 15:49 For someone who’s creating an app to collect orders on their website, if they’re not thinking about data privacy, the thing that people don’t know—who aren’t intimately involved with software development—is how easy it is to hack poorly written code. Again, to be super skeptical: in this day and age, everything is getting hacked. The more AI is accessible, the more hackable your code becomes. Because people can spin up these AI agents with the sole purpose of finding vulnerabilities in software code. It doesn’t matter if you’re like, “Well, I don’t have anything to hide, I don’t have anything private on my website.” It doesn’t matter. They’re going to hack it anyway and start to use it for nefarious things. Katie Robbert – 16:49 One of the things that we—not you and I, but we in my old company—struggled with was conducting those security tests as part of the test plan because we didn’t have someone on the team at the time who was thoroughly skilled in that. Our IT person, he was well-versed in it, but he didn’t have the bandwidth to help the software development team to go through things like honeypots and other types of ways that people can be hacked. But he had the knowledge that those things existed. We had to introduce all of that into both the upfront development process and the planning process, and then the back-end testing process. It added additional time. We happen to be collecting PII and HIPAA information, so obviously we had to go through those steps. Katie Robbert – 17:46 But to even understand the basics of how your code can be hacked is going to be huge. Because it will be hacked if you do not have data privacy and those guardrails around your code. Even if your code is literally just putting up pictures on your website, guess what? Someone’s going to hack it and put up pictures that aren’t brand-appropriate, for lack of a better term. That’s going to happen, unfortunately. And that’s just where we’re at. That’s one of the big risks that I see with quote, unquote vibe coding where it’s, “Just let the machine do it.” If you don’t know what you’re doing, don’t do it. I don’t know how many times I can say that, or at the very. Christopher S. Penn – 18:31 At least know to ask. That’s one of the things. For example, there’s this concept in data security called principle of minimum privilege, which is to grant only the amount of access somebody needs. Same is true for principle of minimum data: collect only information that you actually need. This is an example of a vibe-coded project that I did to make a little Time Zone Tracker. You could put in your time zones and stuff like that. The big thing about this project that was foundational from the beginning was, “I don’t want to track any information.” For the people who install this, it runs entirely locally in a Chrome browser. It does not collect data. There’s no backend, there’s no server somewhere. So it stays only on your computer. Christopher S. Penn – 19:12 The only thing in here that has any tracking whatsoever is there’s a blue link to the Trust Insights website at the very bottom, and that has Google Track UTM codes. That’s it. Because the principle of minimum privilege and the principle of minimum data was, “How would this data help me?” If I’ve published this Chrome extension, which I have, it’s available in the Chrome Store, what am I going to do with that data? I’m never going to look at it. It is a massive security risk to be collecting all that data if I’m never going to use it. It’s not even built in. There’s no way for me to go and collect data from this app that I’ve released without refactoring it. Christopher S. Penn – 19:48 Because we started out with a principle of, “Ain’t going to use it; it’s not going to provide any useful data.” Katie Robbert – 19:56 But that I feel is not the norm. Christopher S. Penn – 20:01 No. And for marketers. Katie Robbert – 20:04 Exactly. One, “I don’t need to collect data because I’m not going to use it.” The second is even if you’re not collecting any data, is your code still hackable so that somebody could hack into this set of code that people have running locally and change all the time zones to be anti-political leaning, whatever messages that they’re like, “Oh, I didn’t realize Chris Penn felt that way.” Those are real concerns. That’s what I’m getting at: even if you’re publishing the most simple code, make sure it’s not hackable. Christopher S. Penn – 20:49 Yep. Do that exercise. Every software language there is has some testing suite. Whether it’s Chrome extensions, whether it’s JavaScript, whether it’s Python, because the human coders who have been working in these languages for 10, 20, 30 years have all found out the hard way that things go wrong. All these automated testing tools exist that can do all this stuff. But when you’re using generative AI, you have to know to ask for it. You have to say. You can say, “Hey, here’s my idea.” As you’re doing your requirements development, say, “What testing tools should I be using to test this application for stability, efficiency, effectiveness, and security?” Those are the big things. That has to be part of the requirements document. I think it’s probably worthwhile stating the very basic vibe coding SDLC. Christopher S. Penn – 21:46 Build your requirements, check your requirements, build a work plan, execute the work plan, and then test until you’re sick of testing, and then keep testing. That’s the process. AI agents and these coding agents can do the “fingers on keyboard” part, but you have to have the knowledge to go, “I need a requirements document.” “How do I do that?” I can have generative AI help me with that. “I need a work plan.” “How do I do that?” Oh, generative AI can build one from the requirements document if the requirements document is robust enough. “I need to implement the code.” “How do I do that?” Christopher S. Penn – 22:28 Oh yeah, AI can do that with a coding agent if it has a work plan. “I need to do QA.” “How do I do that?” Oh, if I have progress logs and the code, AI can do that if it knows what to look for. Then how do I test? Oh, AI can run automated testing utilities and fix the problems it finds, making sure that the code doesn’t drift away from the requirements document until it’s done. That’s the bare bones, bare minimum. What’s missing from that, Katie? From the formal SDLC? Katie Robbert – 23:00 That’s the gist of it. There’s so much nuance and so much detail. This is where, because you and I, we were not 100% aligned on the usage of AI. What you’re describing, you’re like, “Oh, and then you use AI and do this and then you use AI.” To me, that immediately makes me super anxious. You’re too heavily reliant on AI to get it right. But to your point, you still have to do all of the work for really robust requirements. I do feel like a broken record. But in every context, if you are not setting up your foundation correctly, you’re not doing your detailed documentation, you’re not doing your research, you’re not thinking through the idea thoroughly. Katie Robbert – 23:54 Generative AI is just another tool that’s going to get it wrong and screw it up and then eventually collect dust because it doesn’t work. When people are worried about, “Is AI going to take my job?” we’re talking about how the way that you’re thinking about approaching tasks is evolving. So you, the human, are still very critical to this task. If someone says, “I’m going to fire my whole development team, the machines, Vibe code, good luck,” I have a lot more expletives to say with that, but good luck. Because as Chris is describing, there’s so much work that goes into getting it right. Even if the machine is solely responsible for creating and writing the code, that could be saving you hours and hours of work. Because writing code is not easy. Katie Robbert – 24:44 There’s a reason why people specialize in it. There’s still so much work that has to be done around it. That’s the thing that people forget. They think they’re saving time. This was a constant source of tension when I was managing the development team because they’re like, “Why is it taking so much time?” The developers have estimated 30 hours. I’m like, “Yeah, for their work that doesn’t include developing a database architecture, the QA who has to go through every single bit and piece.” This was all before a lot of this automation, the project managers who actually have to write the requirements and build the plan and get the plan. All of those other things. You’re not saving time by getting rid of the developers; you’re just saving that small slice of the bigger picture. Christopher S. Penn – 25:38 The rule of thumb, generally, with humans is that for every hour of development, you’re going to have two to four hours of QA time, because you need to have a lot of extra eyes on the project. With vibe coding, it’s between 10 and 20x. Your hour of vibe coding may shorten dramatically. But then you’re going to. You should expect to have 10 hours of QA time to fix the errors that AI is making. Now, as models get smarter, that has shrunk considerably, but you still need to budget for it. Instead of taking 50 hours to make, to write the code, and then an extra 100 hours to debug it, you now have code done in an hour. But you still need the 10 to 20 hours to QA it. Christopher S. Penn – 26:22 When generative AI spits out that first draft, it’s every other first draft. It ain’t done. It ain’t done. Katie Robbert – 26:31 As we’re wrapping up, Chris, if possible, can you summarize your recent lesson learned from using AI for software development—what is the one thing, the big lesson that you took away? Christopher S. Penn – 26:50 If we think of software development like the floors of a skyscraper, everyone wants the top floor, which is the scenic part. That’s cool, and everybody can go up there. It is built on a foundation and many, many floors of other things. And if you don’t know what those other floors are, your top floor will literally fall out of the sky. Because it won’t be there. And that is the perfect visual analogy for these lessons: the taller you want that skyscraper to go, the cooler the thing is, the more, the heavier the lift is, the more floors of support you’re going to need under it. And if you don’t have them, it’s not going to go well. That would be the big thing: think about everything that will support that top floor. Christopher S. Penn – 27:40 Your overall best practices, your overall coding standards for a specific project, a requirements document that has been approved by the human stakeholders, the work plans, the coding agents, the testing suite, the actual agentic sewing together the different agents. All of that has to exist for that top floor, for you to be able to build that top floor and not have it be a safety hazard. That would be my parting message there. Katie Robbert – 28:13 How quickly are you going to get back into a development project? Christopher S. Penn – 28:19 Production for other people? Not at all. For myself, every day. Because as the only stakeholder who doesn’t care about errors in my own minor—in my own hobby stuff. Let’s make that clear. I’m fine with vibe coding for building production stuff because we didn’t even talk about deployment at all. We touched on it. Just making the thing has all these things. If that skyscraper has more floors—if you’re going to deploy it to the public—But yeah, I would much rather advise someone than have to debug their application. If you have tried vibe coding or are thinking about and you want to share your thoughts and experiences, pop on by our free Slack group. Christopher S. Penn – 29:05 Go to TrustInsights.ai/analytics-for-marketers, where you and over 4,000 other marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, we’re probably there. Go to TrustInsights.ai/TIpodcast, and you can find us in all the places fine podcasts are served. Thanks for tuning in, and we’ll talk to you on the next one. Katie Robbert – 29:31 Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch, and optimizing content strategies. Katie Robbert – 30:24 Trust Insights also offers expert guidance on social media analytics, marketing technology and martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What? livestream webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Katie Robbert – 31:30 Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

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...

Welcome to the CanadianSME Small Business Podcast, hosted by Kripa Anand, where we explore the strategies and technologies that empower businesses to make smarter decisions in the digital age. In this episode, we dive deep into the critical world of data analytics, focusing on bridging the gap between strategy and execution, navigating the future of first-party data with GA4, and operationalizing data privacy without sacrificing marketing performance.Recent trends emphasize the growing importance of data-driven insights, the shift to first-party data amid a cookieless future, and the rising need for privacy-conscious marketing. Our guest, Monika Boldak, Associate Director of Marketing at Napkyn, a trusted digital analytics consultancy and certified Google Marketing Platform Sales Partner, shares expert guidance to help businesses leverage their data effectively and responsibly.Key Highlights:1. Bridging Strategy and Execution: What a strong data foundation really means and why many organizations struggle to connect analytics tools to meaningful business outcomes.2. GA4 and First-Party Data: Common challenges with GA4 adoption, avoiding pitfalls like collecting PII, and future-proofing data strategy with BigQuery and Consent Mode.3. Data Privacy & Marketing Performance: How Canadian businesses can comply with privacy laws like PIPEDA and Quebec's Law 25 while maintaining effective, customer-first marketing strategies.4. Connecting Analytics & Advertising: A success story of improving ad performance and reducing costs by linking offline conversions with Google Ads.5. Upcoming DMFS Canada Summit: Insights on Napkyn's participation and how marketers can responsibly use first-party data to build trust, loyalty, and better marketing outcomes.Special Thanks to Our Partners:RBC: https://www.rbcroyalbank.com/dms/business/accounts/beyond-banking/index.htmlUPS: https://solutions.ups.com/ca-beunstoppable.html?WT.mc_id=BUSMEWAGoogle: https://www.google.ca/For more expert insights, visit www.canadiansme.ca and subscribe to the CanadianSME Small Business Magazine. Stay innovative, stay informed, and thrive in the digital age!Disclaimer: The information shared in this podcast is for general informational purposes only and should not be considered as direct financial or business advice. Always consult with a qualified professional for advice specific to your situation.

What's up everyone, today we have the pleasure of sitting down with Hope Barrett, Sr Director of Product Management, Martech at SoundCloud. Summary: In twelve weeks, Hope led a full messaging stack rebuild with just three people. They cut 200 legacy campaigns down to what mattered, partnered with MoEngage for execution, and shifted messaging into the product org. Now, SoundCloud ships notifications like features that are part of a core product. Governance is clean, data runs through BigQuery, and audiences sync everywhere. The migration was wild and fast, but incredibly meticulous and the ultimate gain was making the whole system make sense again.About HopeHope Barrett has spent the last two decades building the machinery that makes modern marketing work, long before most companies even had names for the roles she was defining. As Senior Director of Product Management for Martech at SoundCloud, she leads the overhaul of their martech stack, making every tool in the chain pull its weight toward growth. She directs both the performance marketing and marketing analytics teams, ensuring the data is not just collected but used with precision to attract fans and artists at the right cost.Before SoundCloud, she spent over six years at CNN scaling their newsletter program into a real asset, not just a vanity list. She laid the groundwork for data governance, built SEO strategies that actually stuck, and made sure editorial, ad sales, and business development all had the same map of who their readers were. Her career also includes time in consulting, digital analytics agencies, and leadership roles at companies like AT&T, Patch, and McMaster-Carr. Across all of them, she has combined technical fluency with sharp business instincts.SoundCloud's Big Messaging Platform Migration and What it Taught Them About Future-Proofing Martech: Diagnosing Broken Martech Starts With Asking Better QuestionsHope stepped into SoundCloud expecting to answer a tactical question: what could replace Nielsen's multi-touch attribution? That was the assignment. Attribution was being deprecated. Pick something better. What she found was a tangle of infrastructure issues that had very little to do with attribution and everything to do with operational blind spots. Messages were going out, campaigns were triggering, but no one could say how many or to whom with any confidence. The data looked complete until you tried to use it for decision-making.The core problem wasn't a single tool. It was a decade of deferred maintenance. The customer engagement platform dated back to 2016. It had been implemented when the vendor's roadmap was still theoretical, so SoundCloud had built their own infrastructure around it. That included external frequency caps, one-off delivery logic, and measurement layers that sat outside the platform. The platform said it sent X messages, but downstream systems had other opinions. Hope quickly saw the pattern: legacy tooling buried under compensatory systems no one wanted to admit existed.That initial audit kicked off a full system teardown. The MMP wasn't viable anymore. Google Analytics was still on Universal. Even the question that brought her in—how to replace MTA—had no great answer. Every path forward required removing layers of guesswork that had been quietly accepted as normal. It was less about choosing new tools and more about restoring the ability to ask direct questions and get direct answers. How many users received a message? What triggered it? Did we actually measure impact or just guess at attribution?“I came in to answer one question and left rebuilding half the stack. You start with attribution and suddenly you're gut-checking everything else.”Hope had done this before. At CNN, she had run full vendor evaluations, owned platform migrations, and managed post-rollout adoption. She knew what bloated systems looked like. She also knew they never fix themselves. Every extra workaround comes with a quiet cost: more dependencies, more tribal knowledge, more reasons to avoid change. Once the platforms can't deliver reliable numbers and every fix depends on asking someone who left last year, you're past the point of iteration. You're in rebuild territory.Key takeaway: If your team can't trace where a number comes from, the stack isn't helping you operate. It's hiding decisions behind legacy duct tape. Fixing that starts with hard questions. Ask what systems your data passes through, which rules live outside the platform, and how long it's been since anyone challenged the architecture. Clarity doesn't come from adding more tools. It comes from stripping complexity until the answers make sense again.Why Legacy Messaging Platforms Quietly Break Your Customer ExperienceHope realized SoundCloud's customer messaging setup was broken the moment she couldn't get a straight answer to a basic question: how many messages had been sent? The platform could produce a number, but it was useless. Too many things happened after delivery. Support infrastructure kicked in. Frequency caps filtered volume. Campaign logic lived outside the actual platform. There was no single system of record. The tools looked functional, but trust had already eroded.The core problem came from decisions made years earlier. The customer engagement platform had been implemented in 2016 when the vendor was still early in its lifecycle. At the time, core features didn't exist, so SoundCloud built their own solutions around it. Frequency management, segmentation logic, even delivery throttling ran outside the tool. These weren't integrations. They were crutches. And they turned what should have been a centralized system into a loosely coupled set of scripts, API calls, and legacy logic that no one wanted to touch.Hope had seen this pattern before. At CNN, she dealt with similar issues and recognized the symptoms immediately. Legacy platforms tend to create debt you don't notice until you start asking precise questions. Things work, but only because internal teams built workarounds that silently age out of relevance. Tech stacks like that don't fail loudly. They fail in fragments. One missing field, one skipped frequency cap, one number that doesn't reconcile across tools. By the time it's clear something's wrong, the actual root cause is buried under six years of operational shortcuts.“The platform gave me a number, but it wasn't the real number. Everything important was happening outside of it.”Hope's philosophy around messaging is shaped by how she defines partnership. She prefers vendors who act like partners, not ticket responders. Partners should care about long-term success, not just contract renewals. But partnership also means using the tool as intended. When the platform is bent around missing features, the relationship becomes strained. Every workaround is a vote of no confidence in the roadmap. Eventually, you're not just managing campaigns. You're managing risk.Key takeaway: If your customer messaging platform can't report true delivery volume because critical logic happens outside of it, you're already in rebuild territory. Don't wait for a total failure. Audit where key rules live. Centralize what matters. And only invest in tools where out-of-the-box features can support your real-world use cases. That way you can grow without outsourcing half your stack to workaround scripts and tribal knowledge.Why Custom Martech Builds Quietly Punish You LaterThe worst part of SoundCloud's legacy stack wasn't the duct-taped infrastructure. It was how long it took to admit it had become a problem. The platform had been in place since 2016, back when the vendor was still figuring out core features. Instead of switching, SoundCloud stayed locked in ...

Balazs Molnar, CEO and co-founder of Rabbit, chats with Kieron Allen about the evolving challenges of cloud cost management and how engineering teams have become central to tackling them. He explains why traditional FinOps tools fall short, how Rabbit dives below the surface to uncover hidden waste (especially in platforms like BigQuery) and why automation is essential for real savings.Optimizing Cloud with RabbitThe Big Themes:Cloud Costs Take Center Stage: Companies are no longer asking, "What can we build on the cloud?" They're now asking, "Why is this so expensive?" Rabbit's origin stems from this exact pivot: cloud costs spiraled out of control, catching businesses off guard. Despite robust migration to cloud environments like Google Cloud, companies found themselves ill-equipped to understand the hidden inefficiencies causing waste. Cloud spend can quickly balloon without the right oversight.The Cloud Buffet Problem: Balazs described cloud computing like a buffet: Engineers can take whatever they want, whenever they want. The cloud's flexibility is its strength but also its greatest risk. Unlike traditional on-prem setups that required hardware purchases and physical limits, cloud environments are boundless. Engineering teams now hold the wheel, yet they're typically not tasked to steer toward efficiency. This creates what Molnar calls a "FinOps trap": assuming finance can solve a problem that's fundamentally technical.Why Optimization Matters Now: Cloud vendors are still growing at impressive rates, but cracks are forming. Some businesses are exiting the cloud, not because they dislike the model — but because costs feel unmanageable. Molnar warns that in most cases, this isn't a cloud problem — it's an optimization problem. The promise of cloud was flexibility and scalability. But without proper tools, it becomes unpredictably expensive.The Big Quote: "We all know the news that cloud vendors are growing 30%+ on a year-over-year basis. But we also started to see cracks in the system where companies are actually deciding to move out of the cloud because it's too expensive to them. But the reality [is] it might not have to be that expensive. It's just not optimized."More from Balazs Molnar and Rabbit:Connect with Balazs on LinkedIn and check out more about Rabbit.* Sponsored podcast * 

Welcome to episode 308 of The Cloud Pod – where the forecast is always cloudy! Justin, Matt and Ryan are in the house today to tell us all about the latest and greatest from FinOps and SnowFlake conferences, plus updates from Security Command Center, OpenAI, and even a new AWS Region. All this and more, today in the cloud!  Titles we almost went with this week: I Left My Wallet at FinOps X, But Found Savings at Snowflake Summit Snowflake City Lights, FinOps by the Sea The Two Summits: A Tale of FinOps and Snowflakes Crunchy on the Outside, Snowflake on the Inside  AWS Taipei: Because Sometimes You Need Your Data Closer Than Your Night Market  AWS Plants Its Flag in Taipei: The 37th Time’s the Charm AWS Slashes GPU Prices Faster Than a CUDA Kernel Two Writers Walk Into a Database… And Both Succeed AWS Network Firewall: Now With Windows! The VPN Connection That Keeps Its Secrets Transform and Roll Out: Pub/Sub’s New Single Message Feature SAP Happens: Google’s New M4 VMs Handle It Better Total Recall: Google’s 6TB Memory Machines The M4trix Has You (And Your In-Memory Databases) DeepSeek and You Shall Find… on Google Cloud Four Score and Seven Vulnerabilities Ago – mk The Fantastic Four Security Features MCP: Model Context Protocol or Master Control Program from Tron? No SQL? No Problem! AI Takes the Wheel Injection Rejection: How Azure Keeps Your Prompts Clean General News  05:09 FinOps X 2025 Cloud Announcements: AI Agents  and Increased FOCUS Support All major cloud providers announced expanded support for FOCUS (FinOps Open Cost and Usage Specification) 1.0, with AWS already in general availability and Google Cloud launching a BigQuery export in private preview.  This signals an industry-wide standardization of cloud cost reporting formats. AWS introduced AI-powered cost optimization through Amazon Q Developer integration with Cost Optimization Hub, enabling automated recommendations across millions of resources with detailed explanations and action plans for cost reduction. Microsoft Azure launched AI agents for application modernization that can reduce migration efforts from months to hours by automating code assessment and remediation across thousands of files, while also introducing flexible PTU reservations that work across multiple AI models. Google Cloud unveiled FinOps Hub 2.0 with Gemini-powered waste detection that identifies underutilized resources (like VMs at 5% usage) and provides AI-generated optimization recommendations for Kubernetes, Cloud Run, and Cloud SQL services. Oracle Cloud Infrastructure added carbon emissio

In this episode, Lois Houston and Nikita Abraham dive into key components of Oracle GoldenGate 23ai with expert insights from Nick Wagner, Senior Director of Product Management.   They break down the Distribution Service, explaining how it moves trail files between environments, replaces the classic extract pump, and ensures secure data transfer. Nick also introduces Target Initiated Paths, a method for connecting less secure environments to more secure ones, and discusses how the Receiver Service simplifies monitoring and management. The episode wraps up with a look into Initial Load, covering different methods for syncing source and target databases without downtime.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs.  Lois: Hey there! Last week, we spoke about the Extract process and today we're going to spend time discussing the Distribution Path, Target Initiated Path, Receiver Server, and Initial Load. These are all critical components of the GoldenGate architecture, and understanding how they work together is essential for successful data replication. 00:58 Nikita: To help us navigate these topics, we've got Nick Wagner joining us again. Nick is a Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Thanks for being with us today. To kick things off, can you tell us what the distribution service is and how it works? Nick: A distribution path is used when we need to send trail files between two different GoldenGate environments. The distribution service replaces the extract pump that was used in GoldenGate classic architecture. And so the distribution service will send the trail files as they're being created to that receiver service and it will write the trail files over on the target system. The distribution service works in a kind of a streaming fashion, so it's constantly pulling the trail files that the extract is creating to see if there's any new data. As soon as it sees new data, it'll packet it up and send it across the network to the receiver service. It can use a couple of different methods to do this. The most secure and recommended method is using a WebSocket secure connection or WSS. If you're going between a microservices and a classic architecture, you can actually tell the distribution service to send it using the classic architecture method. In that case, it's the OGG option when you're configuring the distribution service. There's also some unsecured methods that would send the trail files in plain text. The receiver service is then responsible for taking that data and rewriting it into the trail file on the target site. 02:23 Lois: Nick, what are some of the key features and responsibilities of the distribution service? Nick: It's responsible for command deployment. So any time that you're going to actually make a command to the distribution service, it gets handled there directly. It can handle multiple commands concurrently. It's going to dispatch trail files to one or more receiver servers so you can actually have a single distribution path, send trail files to multiple targets. It can provide some lightweight filtering so you can decide which tables get sent to the target system. And it also is integrated in with our data streams, our pub and subscribe model that we've added in GoldenGate 23ai. 03:01 Lois: Interesting. And are there any protocols to remember when using the distribution service? Nick: We always recommend a secure WebSocket. You also have proxy support for use within cloud environments. And then if you're going to a classic architecture GoldenGate, you would use the Oracle GoldenGate protocol. So in order to communicate with the distribution service and send it commands, you can communicate directly from any web browser, client software-- installation is not required-- or you can also do it through the admin client if necessary, but you can do it directly through browsers. 03:33 Nikita: Ok, let's move on to the target initiated path. Nick, what is it and what does it do essentially? Nick: This is used when you're communicating from a less secure environment to a more secure environment. Often, this requires going through some sort of DMZ. In these situations, a connection cannot be established from the less secure environment into the more secure environment. It actually needs to be established from the more secure environment out. And so if we need to replicate data into a more secure environment, we need to actually have the target GoldenGate environment initiate that connection so that it can be established.  And that's what a target-initiated path does. 04:12 Lois: And how do you set it up? Nick: It's pretty straightforward to set up. You actually don't even need to worry about it on the source side. You actually set it up and configure it from the target. The receiver service is responsible for receiving the trail file data and writing it to the local trail file. In this situation, we have a target-initiated path created. And so that receiver service is going to write the trail files locally and the replicat is going to apply that data into that target system. 04:37 Nikita: I also want to ask you about the Receiver service. What is it really? Nick: Receiver service is pretty straightforward. It's a centrally controlled service. It allows you to view the status of your distribution path and replaces target side collectors that were available in the classic architecture of GoldenGate. You can also get statistics about the receiver service directly from the web UI.  You can get detailed information about these paths by going into the receiver service and identifying information like network details, transfer protocols, how many bytes it's received, how many bytes it's sent out. If you need to issue commands from the admin client to the receiver service, you can use the info command to get details about it. Info all will tell you everything that's running. And you can see that your receiver service is up and running. 05:28 Are you working towards an Oracle Certification this year? Join us at one of our certification prep live events in the Oracle University Learning Community. Get insider tips from seasoned experts and learn from others who have already taken their certifications. Go to community.oracle.com/ou to jump-start your journey towards certification today! 05:53 Nikita: Welcome back. In the last section of today's episode, we'll cover what Initial Load is. Nick, can you break down the basics for us? Nick: So, the initial load is really used when you need to synchronize the source and target systems. Because GoldenGate is designed for 24/7 environments, we need to be able to do that initial load without taking downtime on the source. And so all the methods that we talk about do not require any downtime for that source database. 06:18 Lois: How do you do the initial load? Nick: So there's a couple of different ways to do the initial load. And it really depends on what your topology is. If I'm doing like-to-like replication in a homogeneous environment, we'll say Oracle-to-Oracle, the best options are to use something that's integrated with GoldenGate, some sort of precise instantiation method that does not require HandleCollisions. That's something like a database backup and restoring it to a specific SDN or CSN value using a Database Snapshot. Or in some cases, we can use Oracle Data Pump integration with GoldenGate. There are some less precise instantiation options, which do require HandleCollisions. We also have dissimilar initial load methods. And this is typically when you're going between heterogeneous environments. When my source and target databases don't match and there isn't any kind of fast unload or fast load utility that I could use between those two databases. In almost all cases, this does require HandleCollisions to be used. 07:16 Nikita: Got it. So, with so many options available, are there any advantages to using GoldenGate's own initial load method?  Nick: While some databases do have very good fast load and unload utilities, there are some advantages to using GoldenGate's own initial load method. One, it supports heterogeneous replication environments. So if I'm going from Postgres to Oracle, it'll do all the data type transformation, character set transformation for me. It doesn't require any downtime, if certain conditions are met.  It actually performs transformation as the data is loaded, too, as well as filtering. And so any transformation that you would be doing in your normal transaction log replication or CDC replication can also go through the same transformation for the initial load process. GoldenGate's initial load process does read directly from the source tables. And it fetches the data in arrays. It also uses parallel processing to speed up the replication. It does also handle activity on the source tables during the initial load process, so you do not need to worry about quiescing that source database. And a lot of the initial load methods directly built into GoldenGate support distributed application analytics targets, including things like Databricks, Snowflake, BigQuery. 08:28 Lois: And what about its limitations? Or to put it differently, when should users consider using different methods? Nick: So the first thing to consider is system proximity. We want to make sure that the two systems we're working with are close together. Or if not, how are we going to send the data across? One thing to keep in mind, when we do the initial load, the source database is not quiesced. So if it takes an hour to do the initial load or 10 hours, it really doesn't matter to GoldenGate. So that's something to keep in mind. Even though we talk about performance of this, the performance really isn't as critical as one might suspect. So the important thing about data system proximity is the proximity to the extract and replicat processes that are going to be pulling the data out and pushing it across. And then how much data is generated? Are we talking about a database that's just a couple of gigabytes? Or are we talking about a database that's hundreds of terabytes? Do we want to consider outage time? Would it be faster to take a little bit of outage and use some other method to move the data across? What kind of outage or downtime windows do we have for these environments? And then another consideration is disk space. As we're pulling the data out of that source database, we need to have somewhere to store it. And if we don't have enough disk space, we need to run to temporary space or to use multiple external drives to be able to support it. So these are all different considerations. 09:50 Nikita: I think we can wind up our episode with that. Thanks, Nick, for giving us your insights.  Lois: If you'd like to learn more about the topics we covered today, head over to mylearn.oracle.com and check out the Oracle GoldenGate 23ai: Fundamentals course. Nikita: In our next episode, Nick will take us through the Replicat process. Until then, this is Nikita Abraham… Lois: And, Lois Houston signing off! 10:14 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

In this episode, Lois Houston and Nikita Abraham continue their deep dive into Oracle GoldenGate 23ai, focusing on its evolution and the extensive features it offers. They are joined once again by Nick Wagner, who provides valuable insights into the product's journey.   Nick talks about the various iterations of Oracle GoldenGate, highlighting the significant advancements from version 12c to the latest 23ai release. The discussion then shifts to the extensive new features in 23ai, including AI-related capabilities, UI enhancements, and database function integration.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   -----------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! Last week, we introduced Oracle GoldenGate and its capabilities, and also spoke about GoldenGate 23ai. In today's episode, we'll talk about the various iterations of Oracle GoldenGate since its inception. And we'll also take a look at some new features and the Oracle GoldenGate product family. 00:57 Lois: And we have Nick Wagner back with us. Nick is a Senior Director of Product Management for GoldenGate at Oracle. Hi Nick! I think the last time we had an Oracle University course was when Oracle GoldenGate 12c was out. I'm sure there's been a lot of advancements since then. Can you walk us through those? Nick: GoldenGate 12.3 introduced the microservices architecture. GoldenGate 18c introduced support for Oracle Autonomous Data Warehouse and Autonomous Transaction Processing Databases. In GoldenGate 19c, we added the ability to do cross endian remote capture for Oracle, making it easier to set up the GoldenGate OCI service to capture from environments like Solaris, Spark, and HP-UX and replicate into the Cloud. Also, GoldenGate 19c introduced a simpler process for upgrades and installation of GoldenGate where we released something called a unified build. This means that when you install GoldenGate for a particular database, you don't need to worry about the database version when you install GoldenGate. Prior to this, you would have to install a version-specific and database-specific version of GoldenGate. So this really simplified that whole process. In GoldenGate 23ai, which is where we are now, this really is a huge release.  02:16 Nikita: Yeah, we covered some of the distributed AI features and high availability environments in our last episode. But can you give us an overview of everything that's in the 23ai release? I know there's a lot to get into but maybe you could highlight just the major ones? Nick: Within the AI and streaming environments, we've got interoperability for database vector types, heterogeneous capture and apply as well. Again, this is not just replication between Oracle-to-Oracle vector or Postgres to Postgres vector, it is heterogeneous just like the rest of GoldenGate. The entire UI has been redesigned and optimized for high speed. And so we have a lot of customers that have dozens and dozens of extracts and replicats and processes running and it was taking a long time for the UI to refresh those and to show what's going on within those systems. So the UI has been optimized to be able to handle those environments much better. We now have the ability to call database functions directly from call map. And so when you do transformation with GoldenGate, we have about 50 or 60 built-in transformation routines for string conversion, arithmetic operation, date manipulation. But we never had the ability to directly call a database function. 03:28 Lois: And now we do? Nick: So now you can actually call that database function, database stored procedure, database package, return a value and that can be used for transformation within GoldenGate. We have integration with identity providers, being able to use token-based authentication and integrate in with things like Azure Active Directory and your other single sign-on for the GoldenGate product itself. Within Oracle 23ai, there's a number of new features. One of those cool features is something called lock-free reservation columns. So this allows you to have a row, a single row within a table and you can identify a column within that row that's like an inventory column. And you can have multiple different users and multiple different transactions all updating that column within that same exact row at that same time. So you no longer have row-level locking for these reservation columns. And it allows you to do things like shopping carts very easily. If I have 500 widgets to sell, I'm going to let any number of transactions come in and subtract from that inventory column. And then once it gets below a certain point, then I'll start enforcing that row-level locking. 04:43 Lois: That's really cool… Nick: The one key thing that I wanted to mention here is that because of the way that the lock-free reservations work, you can have multiple transactions open on the same row. This is only supported for Oracle to Oracle. You need to have that same lock-free reservation data type and availability on that target system if GoldenGate is going to replicate into it. 05:05 Nikita: Are there any new features related to the diagnosability and observability of GoldenGate?  Nick: We've improved the AWR reports in Oracle 23ai. There's now seven sections that are specific to Oracle GoldenGate to allow you to really go in and see exactly what the GoldenGate processes are doing and how they're behaving inside the database itself. And there's a Replication Performance Advisor package inside that database, and that's been integrated into the Web UI as well. So now you can actually get information out of the replication advisor package in Oracle directly from the UI without having to log into the database and try to run any database procedures to get it. We've also added the ability to support a per-PDB Extract.  So in the past, when GoldenGate would run on a multitenant database, a multitenant database in Oracle, all the redo data from any pluggable database gets sent to that one redo stream. And so you would have to configure GoldenGate at the container or root level and it would be able to access anything at any PDB. Now, there's better security and better performance by doing what we call per-PDB Extract. And this means that for a single pluggable database, I can have an extract that runs at that database level that's going to capture information just from that pluggable database. 06:22 Lois And what about non-Oracle environments, Nick? Nick: We've also enhanced the non-Oracle environments as well. For example, in Postgres, we've added support for precise instantiation using Postgres snapshots. This eliminates the need to handle collisions when you're doing Postgres to Postgres replication and initial instantiation. On the GoldenGate for big data side, we've renamed that product more aptly to distributed applications in analytics, which is really what it does, and we've added a whole bunch of new features here too. The ability to move data into Databricks, doing Google Pub/Sub delivery. We now have support for XAG within the GoldenGate for distributed applications and analytics. What that means is that now you can follow all of our MAA best practices for GoldenGate for Oracle, but it also works for the DAA product as well, meaning that if it's running on one node of a cluster and that node fails, it'll restart itself on another node in the cluster. We've also added the ability to deliver data to Redis, Google BigQuery, stage and merge functionality for better performance into the BigQuery product. And then we've added a completely new feature, and this is something called streaming data and apps and we're calling it AsyncAPI and CloudEvent data streaming. It's a long name, but what that means is that we now have the ability to publish changes from a GoldenGate trail file out to end users. And so this allows through the Web UI or through the REST API, you can now come into GoldenGate and through the distributed applications and analytics product, actually set up a subscription to a GoldenGate trail file. And so this allows us to push data into messaging environments, or you can simply subscribe to changes and it doesn't have to be the whole trail file, it can just be a subset. You can specify exactly which tables and you can put filters on that. You can also set up your topologies as well. So, it's a really cool feature that we've added here. 08:26 Nikita: Ok, you've given us a lot of updates about what GoldenGate can support. But can we also get some specifics? Nick: So as far as what we have, on the Oracle Database side, there's a ton of different Oracle databases we support, including the Autonomous Databases and all the different flavors of them, your Oracle Database Appliance, your Base Database Service within OCI, your of course, Standard and Enterprise Edition, as well as all the different flavors of Exadata, are all supported with GoldenGate. This is all for capture and delivery. And this is all versions as well. GoldenGate supports Oracle 23ai and below. We also have a ton of non-Oracle databases in different Cloud stores. On an non-Oracle side, we support everything from application-specific databases like FairCom DB, all the way to more advanced applications like Snowflake, which there's a vast user base for that. We also support a lot of different cloud stores and these again, are non-Oracle, nonrelational systems, or they can be relational databases. We also support a lot of big data platforms and this is part of the distributed applications and analytics side of things where you have the ability to replicate to different Apache environments, different Cloudera environments. We also support a number of open-source systems, including things like Apache Cassandra, MySQL Community Edition, a lot of different Postgres open source databases along with MariaDB. And then we have a bunch of streaming event products, NoSQL data stores, and even Oracle applications that we support. So there's absolutely a ton of different environments that GoldenGate supports. There are additional Oracle databases that we support and this includes the Oracle Metadata Service, as well as Oracle MySQL, including MySQL HeatWave. Oracle also has Oracle NoSQL Spatial and Graph and times 10 products, which again are all supported by GoldenGate. 10:23 Lois: Wow, that's a lot of information! Nick: One of the things that we didn't really cover was the different SaaS applications, which we've got like Cerner, Fusion Cloud, Hospitality, Retail, MICROS, Oracle Transportation, JD Edwards, Siebel, and on and on and on.  And again, because of the nature of GoldenGate, it's heterogeneous. Any source can talk to any target. And so it doesn't have to be, oh, I'm pulling from Oracle Fusion Cloud, that means I have to go to an Oracle Database on the target, not necessarily.  10:51 Lois: So, there's really a massive amount of flexibility built into the system.  11:00 Unlock the power of AI Vector Search with our new course and certification. Get more accurate search results, handle complex datasets easily, and supercharge your data-driven decisions. From now through May 15, 2025, we are waiving the certification exam fee (valued at $245). Visit mylearn.oracle.com to enroll. 11:26 Nikita: Welcome back! Now that we've gone through the base product, what other features or products are in the GoldenGate family itself, Nick? Nick: So we have quite a few. We've kind of touched already on GoldenGate for Oracle databases and non-Oracle databases. We also have something called GoldenGate for Mainframe, which right now is covered under the GoldenGate for non-Oracle, but there is a licensing difference there. So that's something to be aware of. We also have the OCI GoldenGate product. We are announcing and we have announced that OCI GoldenGate will also be made available as part of the Oracle Database@Azure and Oracle Database@ Google Cloud partnerships.  And then you'll be able to use that vendor's cloud credits to actually pay for the OCI GoldenGate product. One of the cool things about this is it will have full feature parity with OCI GoldenGate running in OCI. So all the same features, all the same sources and targets, all the same topologies be able to migrate data in and out of those clouds at will, just like you do with OCI GoldenGate today running in OCI.  We have Oracle GoldenGate Free.  This is a completely free edition of GoldenGate to use. It is limited on the number of platforms that it supports as far as sources and targets and the size of the database.  12:45 Lois: But it's a great way for developers to really experience GoldenGate without worrying about a license, right? What's next, Nick? Nick: We have GoldenGate for Distributed Applications and Analytics, which was formerly called GoldenGate for big data, and that allows us to do all the streaming. That's also where the GoldenGate AsyncAPI integration is done. So in order to publish the GoldenGate trail files or allow people to subscribe to them, it would be covered under the Oracle GoldenGate Distributed Applications and Analytics license. We also have OCI GoldenGate Marketplace, which allows you to run essentially the on-premises version of GoldenGate but within OCI. So a little bit more flexibility there. It also has a hub architecture. So if you need that 99.99% availability, you can get it within the OCI Marketplace environment. We have GoldenGate for Oracle Enterprise Manager Cloud Control, which used to be called Oracle Enterprise Manager. And this allows you to use Enterprise Manager Cloud Control to get all the statistics and details about GoldenGate. So all the reporting information, all the analytics, all the statistics, how fast GoldenGate is replicating, what's the lag, what's the performance of each of the processes, how much data am I sending across a network. All that's available within the plug-in. We also have Oracle GoldenGate Veridata. This is a nice utility and tool that allows you to compare two databases, whether or not GoldenGate is running between them and actually tell you, hey, these two systems are out of sync. And if they are out of sync, it actually allows you to repair the data too. 14:25 Nikita: That's really valuable…. Nick: And it does this comparison without locking the source or the target tables. The other really cool thing about Veridata is it does this while there's data in flight. So let's say that the GoldenGate lag is 15 or 20 seconds and I want to compare this table that has 10 million rows in it. The Veridata product will go out, run its comparison once. Once that comparison is done the first time, it's then going to have a list of rows that are potentially out of sync. Well, some of those rows could have been moved over or could have been modified during that 10 to 15 second window. And so the next time you run Veridata, it's actually going to go through. It's going to check just those rows that were potentially out of sync to see if they're really out of sync or not. And if it comes back and says, hey, out of those potential rows, there's two out of sync, it'll actually produce a script that allows you to resynchronize those systems and repair them. So it's a very cool product.  15:19 Nikita: What about GoldenGate Stream Analytics? I know you mentioned it in the last episode, but in the context of this discussion, can you tell us a little more about it?  Nick: This is the ability to essentially stream data from a GoldenGate trail file, and they do a real time analytics on it. And also things like geofencing or real-time series analysis of it.  15:40 Lois: Could you give us an example of this? Nick: If I'm working in tracking stock market information and stocks, it's not really that important on how much or how far down a stock goes. What's really important is how quickly did that stock rise or how quickly did that stock fall. And that's something that GoldenGate Stream Analytics product can do. Another thing that it's very valuable for is the geofencing. I can have an application on my phone and I can track where the user is based on that application and all that information goes into a database. I can then use the geofencing tool to say that, hey, if one of those users on that app gets within a certain distance of one of my brick-and-mortar stores, I can actually send them a push notification to say, hey, come on in and you can order your favorite drink just by clicking Yes, and we'll have it ready for you. And so there's a lot of things that you can do there to help upsell your customers and to get more revenue just through GoldenGate itself. And then we also have a GoldenGate Migration Utility, which allows customers to migrate from the classic architecture into the microservices architecture. 16:44 Nikita: Thanks Nick for that comprehensive overview.  Lois: In our next episode, we'll have Nick back with us to talk about commonly used terminology and the GoldenGate architecture. And if you want to learn more about what we discussed today, visit mylearn.oracle.com and take a look at the Oracle GoldenGate 23ai Fundamentals course. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 17:10 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

This week, Frank sat down with Dr. Jacob Leverich—Stanford PhD, cofounder of Observe, and a veteran of the Google MapReduce team and Splunk. Jacob's journey, from tinkering with video game code as a kid, to innovating at the cutting edge of distributed systems and energy efficiency, is as inspiring as it is informative.Key TakeawaysEarly Tech Roots: Hear how curiosity with QBasic and classic PCs (think IBM PCXT and Commodore) put Jacob on a path to high-impact data engineering.MapReduce, Dremel, & the Rise of Big Data: Jacob pulls back the curtain on working with some of the most influential data processing tools at Google and how these systems shifted the entire data landscape (hello, BigQuery!).Building Efficient Systems: It's not just about scale—energy efficiency and performance optimization are the unsung heroes of today's data infrastructure. Jacob explains why making things “just work” isn't enough anymore.The Realities of Ops & Observability: Remember the days of grepping logs at 2AM? There's a better way. Jacob shares how platforms like Observe help teams consolidate, visualize, and act on operational data—turning chaos into actionable insight.Bridging Data & Ops: The lines between data observability and traditional ops are blurring, and Jacob's unique experience shows how best practices from data warehousing are finally making ops smoother (and less sleepless).Power Concerns & the Future: As data grows, so does energy consumption in data centers. Find out why optimization isn't just good for performance—it's key to sustainability.Timestamps00:00 Interview with Jacob Levrich05:59 Journey into Game Programming06:43 "Pursuing Fast Video Game Code"10:23 Data Processing and Power Efficiency16:11 Snowflake's Transformative Database Approach19:18 Journey to Data Management Industry21:37 Data Products: Solving Core Challenges27:07 Early Web Log Analysis Techniques28:57 Consolidating Data for Efficiency33:23 Specialized Tools and Context Switching35:43 Unique Dual-Expertise in Tech38:58 User-Centric Business Strategies42:13 IP Data Analysis in Cloud47:23 Electricity Transport Upsets Local Farms48:25 Shift to Parallel Computing52:10 Hardware Specialization & Software Optimization57:32 "Stay Data Driven"

In this episode we discuss the latest and greatest announcements from the Google Next 2025 conference with Simon Pane (Oracle ACE and Google Cloud Champion), Nelson Calero (Oracle Ace Director) and Jeff Deverter (Pythian Field CTO). We go over Oracle partnership updates, BigQuery updates, AlloyDb updates and of course, AI announcements!

Welcome to episode 298 of The Cloud Pod – where the forecast is always cloudy! Justin, Matthew and Ryan are in the house (and still very much missing Jonathan) to bring you a  jam packed show this week, with news from Beijing to Virginia! Did you know Virginia was in the US? Amazon definitely wants you to know that.  We've got updates from BigQuery Git Support and their new collab tools, plus all the AI updates you were hoping you'd miss. Tune in now!  Titles we almost went with this week: The Cloud Pod now Recorded from Planet Earth Wait Java still exists? When will java just be coffee and not software Cloudflare Makes AI beat Mazes Replacing native mobile things with mobile web apps won't fix your problems AWS Turn your security over to the bots The Cloud Pod is lost in the AI labyrinth  AI security agents to secure the AI… wait recursion Durable + Stateless.. I don't know if you know what those words means Click ops expands to our phones yay! The Cloud Pod is now a data analyst  Gitops come to bigquery A big thanks to this week's sponsor: We're sponsorless! Want to get your brand, company, or service in front of a very enthusiastic group of cloud news seekers? You've come to the right place! Send us an email or hit us up on our slack channel for more info.  AI Is Going Great – Or How ML Makes All Its Money   00:46 Manus, a New AI Agent From China is Going Viral—And Raising Big Questions   Manus is being described as “the first true autonomous AI agent” from China, capable of completing weeks of professional work in hours. Developed by a team called Butterfly Effect with offices in Beijing and Wuhan, Manus functions as a truly autonomous agent that independently analyzes, plans, and executes complex tasks.  The system uses a multi-agent architecture powered by several distinct AI models, including Anthropic’s Claude 3.5 Sonnet and fine-tuned versions of

Send us a textLet's demystify the magic behind streamlined customer success operations. In this episode of the Customer Success Playbook podcast, Kevin Metzger sits down with Gilad Shriki from Scope to unpack their strategic integration of FunnelStory. They dive into privacy-first data management, lightning-fast time-to-value, and how AI is reshaping how teams interact with data. Plus, find out why Gilad believes FunnelStory might just be the one platform to rule them all.Detailed Description with Business Insights: In this engaging episode of the Customer Success Playbook, Kevin Metzger interviews Gilad Shriki, Head of Customer Experience at Scope, who offers a real-world case study of successfully implementing FunnelStory. With Roman Trebon off this week, Kevin navigates a thoughtful conversation that brings valuable technical and strategic takeaways to customer success leaders.Gilad breaks down how Scope maintains data privacy by leveraging a custom anonymization layer before syncing anonymized data into BigQuery. From there, FunnelStory becomes the centerpiece of their CS tech stack, tightly integrated with HubSpot and Segment. The result? A seamless, compliant, and highly performant system that delivers actionable insights with minimal setup.The discussion peels back the curtain on modern data stack integrations, emphasizing the importance of time-to-value and the benefits of designing for automation-first customer success platforms. Gilad candidly explains how FunnelStory outperformed expectations by offering an intuitive plug-and-play experience and how its engineering team's responsiveness created a frictionless implementation.Most notably, Gilad envisions FunnelStory not just as a visibility tool but as a centralized hub for both automation and human interaction. His goal? A single pane of glass where CSMs manage sentiment, risk, and engagement—without needing to bolt on other platforms like Gainsight.If you're scaling a CS org or rethinking your tech stack, this episode is your playbook for staying lean without sacrificing power. Tune in and learn how a privacy-first, AI-powered, integrated system can revolutionize how you scale customer success.Now you can interact with us directly by leaving a voice message at https://www.speakpipe.com/CustomerSuccessPlaybookPlease Like, Comment, Share and Subscribe. You can also find the CS Playbook Podcast:YouTube - @CustomerSuccessPlaybookPodcastTwitter - @CS_PlaybookYou can find Kevin at:Metzgerbusiness.com - Kevin's person web siteKevin Metzger on Linked In.You can find Roman at:Roman Trebon on Linked In.

Send us a textIn this engaging episode of the Customer Success Playbook Podcast, host Kevin Metzger sits down with Gilad Shriki from The Scope to explore how FunnelStory is transforming customer success operations. With seamless integration capabilities and a robust automation-first approach, FunnelStory is setting a new standard for customer success platforms.Gilad shares insights into how his team successfully integrated FunnelStory with BigQuery, HubSpot, and Segment, all while maintaining strict data privacy protocols. He also discusses how AI-driven automation is enhancing customer sentiment analysis and churn prediction, giving CS teams an edge in proactive engagement.Is Funnel Story truly a one-stop shop for customer success? Can businesses of all sizes leverage its automation without sacrificing human interaction? Listen in as Gilad provides a firsthand account of his experience and why he believes FunnelStory is reshaping the future of customer success management.Detailed Episode Insights:Seamless Integration: How The Scope connected FunnelStory with their existing data stack while maintaining PII privacy.Automation at the Core: Why starting with automation before layering in human interaction changes the game for CS teams.AI-Powered Efficiency: How FunnelStory is accelerating time-to-value and making predictive insights more accessible.Scalability & Growth: Can FunnelStory support businesses up to $500M in revenue? Gilad shares his perspective.The Future of CS Tech: What's next for AI-powered customer success platforms?Now you can interact with us directly by leaving a voice message at https://www.speakpipe.com/CustomerSuccessPlaybookPlease Like, Comment, Share and Subscribe. You can also find the CS Playbook Podcast:YouTube - @CustomerSuccessPlaybookPodcastTwitter - @CS_PlaybookYou can find Kevin at:Metzgerbusiness.com - Kevin's person web siteKevin Metzger on Linked In.You can find Roman at:Roman Trebon on Linked In.

Topics covered in this episode: LLM Catcher On PyPI Quarantine process RESPX Unpacking kwargs with custom objects Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: LLM Catcher via Pat Decker Large language model diagnostics for python applications and FastAPI applications . Features Exception diagnosis using LLMs (Ollama or OpenAI) Support for local LLMs through Ollama OpenAI integration for cloud-based models Multiple error handling approaches: Function decorators for automatic diagnosis Try/except blocks for manual control Global exception handler for unhandled errors from imported modules Both synchronous and asynchronous APIs Flexible configuration through environment variables or config file Brian #2: On PyPI Quarantine process Mike Fiedler Project Lifecycle Status - Quarantine in his "Safety & Security Engineer: First Year in Review post” Some more info now in Project Quarantine Reports of malware in a project kick things off Admins can now place a project in quarantine, allowing it to be unavailable for install, but still around for analysis. New process allows for packages to go back to normal if the report is false. However Since August, the Quarantine feature has been in use, with PyPI Admins marking ~140 reported projects as Quarantined. Of these, only a single project has exited Quarantine, others have been removed. Michael #3: RESPX Mock HTTPX with awesome request patterns and response side effects A simple, yet powerful, utility for mocking out the HTTPX, and HTTP Core, libraries. Start by patching HTTPX, using respx.mock, then add request routes to mock responses. For a neater pytest experience, RESPX includes a respx_mock fixture Brian #4: Unpacking kwargs with custom objects Rodrigo A class needs to have a keys() method that returns an iterable. a __getitem__() method for lookup Then double splat ** works on objects of that type. Extras Brian: A surprising thing about PyPI's BigQuery data - Hugovk Top PyPI Packages (and therefore also Top pytest Plugins) uses a BigQuery dataset Has grabbed 30-day data of 4,000, then 5,000, then 8,000 packages. Turns out 531,022 packages (amount returned when limit set to a million) is the same cost. So…. hoping future updates to these “Top …” pages will have way more data. Also, was planning on recording a Test & Code episode on pytest-cov today, but haven't yet. Hopefully at least a couple of new episodes this week. Finally updated pythontest.com with BlueSky links on home page and contact page. Michael: Follow up from Owen (uv-secure): Thanks for the multiple shout outs! uv-secure just uses the PyPi json API at present to query package vulnerabilities (same as default source for pip audit). I do smash it asynchronously for all dependencies at once... but it still takes a few seconds. Joke: Bugs hide from the light!

Send us a textWeb Crawler DesignsCan a simple idea like building a web crawler teach you the intricacies of system design? Join me, Ben Kitchell, as we uncover this fascinating intersection. Returning from a brief pause, I'm eager to guide you through the essential building blocks of a web crawler, from queuing seed URLs to parsing new links autonomously. These basic functionalities are your gateway to creating a minimum viable product or acing that system design interview. You'll gain insights into potential extensions like scheduled crawling and page prioritization, ensuring a strong foundation for tackling real-world challenges.Managing a billion URLs a month is no small feat, and scaling such a system requires meticulous planning. We'll break down the daunting numbers into digestible pieces, exploring how to efficiently store six petabytes of data annually. By examining different database models, you'll learn how to handle URLs, track visit timestamps, and keep data searchable. The focus is on creating a robust system that not only scales but does so in a way that meets evolving demands without compromising on performance.Navigating the complexities of designing a web crawler means making critical decisions about data storage and system architecture. We'll weigh the benefits of using cloud storage solutions like AWS S3 and Azure Blob Storage against maintaining dedicated servers. Discover the role of REST APIs in seamless user and service interactions, and explore search functionalities using Cassandra, Amazon Athena, or Google's BigQuery. Flexibility and foresight are key as we build systems that adapt to future needs. Thank you for your continued support—let's keep learning and growing on this exciting system design journey together.Support the showDedicated to the memory of Crystal Rose.Email me at LearnSystemDesignPod@gmail.comJoin the free Discord Consider supporting us on PatreonSpecial thanks to Aimless Orbiter for the wonderful music.Please consider giving us a rating on ITunes or wherever you listen to new episodes.

Google Cloud's Innovation and GrowthThe Big Themes:Google Cloud's record growth and market positioning: In 2024, Google Cloud experienced five consecutive quarters of accelerating growth, including a remarkable 35% growth in Q3, up from 29% in Q2. Kurian attributes this success to the company's ability to listen to customers, innovate with products that meet their evolving needs, and strategically invest in a strong go-to-market organization.AI cost reduction and efficiency: Kurian comments on Google Cloud's efforts to significantly reduce the cost of AI models. Through improved software stack capabilities and optimizations, Google has decreased the cost of AI by more than 10x in just six months. Reducing latency, improving response accuracy, and utilizing distillation (e.g., making models run on smaller devices like phones) have contributed to lowering operational costs while increasing model efficiency. This approach has resulted in a 15-17x growth in model usage in just five months.The evolving role of cloud in business transformation: Kurian notes a fundamental shift in how businesses view cloud computing. Initially seen as a way to reduce costs, cloud is now seen as a tool for driving business transformation. AI, analytics, and security capabilities are helping organizations speed up decision-making, optimize logistics, and gain competitive advantages. Kurian believes that the next wave of cloud adoption will focus more on enabling new business models, products, and markets rather than just reducing IT costs.The Big Quote: “We tend to look ahead by listening to customers and understanding their needs, and create in a disciplined way, new product offerings. If you look a the last five years, we've introduced enough steady cadence. First, we started with infrastructure, then we added databases to it. We used our strength with BigQuery to build out an analytics portfolio. We were one of the earliest to say . . . we should not only provide [customers] a secure cloud, but we should also build a security product portfolio. Every one of those has driven diversification of our revenue stream."

In this Checkout episode, we sit down with Jethro Marks, co-founder of The Nile, to uncover personal insights behind this pioneering ecom giant. Jethro shares his thoughts on disruptive platforms like Temu, his admiration for the logistics mastery of Dan Murphy's and the critical role Google's BigQuery is playing in powering The Nile. He also reflects on how balancing innovation with consistency has fed into the brand's long-term success amidst the ever-changing ecom landscape.Check out our full-length interview with Jethro Marks here:How Jethro Marks is Transforming The Nile into a Leading Aussie Online Bookstore | #454 This episode was brought to you by:Deliver In PersonShopify PlusAbout your guest:Jethro Marks is the Co-Founder and CEO of The Nile, one of Australia's pioneering pure-play online retailers. With over 15 years of experience in eCommerce, Jethro has been there since the start with co-founder Mark Taylor, taking the enterprise from a living room with two guys and a computer to a global operation across Australia, New Zealand, the US, and UK, offering over 40 million products. A former Director of NORA, he is also a Non-Executive Director at DroneShield (ASX: DRO).About your host:Nathan Bush is the host of the Add To Cart podcast and a leading ecommerce transformation consultant. He has led eCommerce for businesses with revenue $100m+ and has been recognised as one of Australia's Top 50 People in eCommerce four years in a row. You can contact Nathan on LinkedIn, Twitter or via email.Please contact us if you: Want to come on board as an Add To Cart sponsor Are interested in joining Add To Cart as a co-host Have any feedback or suggestions on how to make Add To Cart betterEmail hello@addtocart.com.au We look forward to hearing from you! Hosted on Acast. See acast.com/privacy for more information.

Bayer's Data Evolution with AlloyDBThe Big Themes:Data complexity and intelligent agriculture: Bayer Crop Science is addressing agriculture's complex data challenges. The company integrates data such as satellite imagery, weather conditions, soil data, and IoT device inputs, to drive innovation in seed development and farming practices. By leveraging cloud technologies like AlloyDB, Bayer's teams can support the future of farming, despite challenges posed by climate change and rising global food demand.Integrating BigQuery for comprehensive analytics: To further enhance its data-driven insights, Bayer integrates Google BigQuery alongside AlloyDB for extensive data analysis. BigQuery serves as the central analytics warehouse, receiving billions of phenotypic data points for in-depth modeling and decision-making. During harvest season, Bayer can quickly access and analyze comprehensive datasets, enabling better decisions across production and supply chains.Harvest season demands and system resilience: During harvest season, Bayer Crop Science faces intense pressure as high volumes of data flow in, requiring real-time analysis and decision-making. The peak demand period sees a sharp increase in read and write operations, making it essential for Bayer's data system to function seamlessly. AlloyDB played a crucial role in handling these spikes by providing low-latency data processing and high availability.The Big Quote: “Climate change is a new challenge. You see some of these forecasts coming out of academia that yields will go down by 30% — that will arrest this great trend that we've seen continually increasing over the last 100 years. We need to solve for that, and that's going to take new types of data and new approaches and these types of things."

لو خطر في بالك قبل كده ليه عندنا كل قواعد البيانات دي, و ليه فيه منهم انواع مختلفة DBMS, NOSQL و غيرهم, طيب الناس اللي بتشتغل على الحاجات دي ايه التحديات اللي بيواجهوها, و ايه التخصص ده و ايه المتطلبات بتاعته. Ahmed Ayad is a SQL Engineer by trade, a database guy by education and training, and data dude by passion. I am currently an Engineering Director of the Managed Storage and Workload Management team in Google #BigQuery, building the best large scale enterprise data warehouse on the planet. My team owns the core parts of BigQuery involved in managing user data, metadata catalog, streaming and batch ingestion, replication, resource management and placement, physical sharding, and structured lake analytics. Over the years we have: - Grew data under management by several orders of magnitude. - Grew BigQuery's global footprint to more than 20+ regions and counting. - Enabled the hyper scaling of data analytics for a Who's Who list of Fortune 500 users, both Enterprise and Cloud-native. I am passionate about building cool technologies at scale, and the effective teams that create them. Things I did in previous professional lives: - I have shipped components in SQL Server product since SQL Server 2008. Worked on the Performance Data Collector, Policy Based Management, AlwaysOn, The Utility Control Point, SQL Azure stack from the backend to the middle-tier and Portal, SQL Server Agent, SQL Server Optimizer, and SQL Server Management Tools. - Did Database research in the areas of Data Mining, Query Optimization, and Data Streaming.

I am excited to bring you an insightful conversation with Russell Efird, Head of North American Partnerships at Quantum Metric, recorded live from Google Cloud's Marketplace Exchange! Russell dives into how Quantum Metric, a digital analytics experience platform, leverages the power of Google Cloud technologies like BigQuery and Gen AI to create seamless, high-performing digital journeys that resonate with C-level leaders and drive real business outcomes. Russell shares invaluable insights into the evolving enterprise buying landscape and the importance of aligning SaaS solutions to meet the needs of key decision-makers, from Chief Digital Officers to Heads of E-commerce. He highlights Quantum Metric's strategy of building “value networks” by collaborating with Google and other ISVs, enhancing the customer experience and accelerating business impact through innovative partnerships. Packed with practical strategies for growth, marketplace success, and ecosystem collaboration, this episode of The Ultimate Guide to Partnering is a must-watch for anyone invested in partnerships or digital analytics. Tune in for Russell's expert advice on building a future-focused partner strategy and driving growth through meaningful, multi-partner collaborations!

In this episode of SEO Cash Flow, it's me, Olga Zarr, teaming up with Myriam Jessier to tackle BigQuery for SEOs. We're diving into how you can pull more insights out of Google Search Console data without turning into a data scientist. Myriam's going all-in on learning BigQuery, while I'm sticking to my minimalist, ADHD-friendly approach—keeping it simple, powerful, and quick. We chat about why BigQuery isn't as scary as it seems and how it can give you way more control over your data, letting you see past the usual Google limits. This is for SEOs who want that edge without a ton of fuss or coding. If you've been wanting to get into BigQuery but didn't know where to start, this episode is your roadmap. Follow Myriam Jessier:

Welcome to episode 279 of The Cloud Pod, where the forecast is always cloudy! This week Justin, Jonathan and Matthew are your guide through the Cloud. We're talking about everything from BigQuery to Google Nuclear power plans, and everything in between! Welcome to episode 279!  Titles we almost went with this week: AWS SKYNET (Q) now controls the supply chain AWS Supply Chain: Where skynet meets your shopping list Digital Ocean follows Azure with the Premium everything EKS mounts S3  GCP now a nuclear Big query don't hit that iceberg  Big Query Yells: “ICEBERG AHEAD”  The Cloud Pod: Now with 50% more meltdown protection The Cloud Pod radiates excitement over Google's nuclear deal A big thanks to this week's sponsor: We're sponsorless! Want to get your brand, company, or service in front of a very enthusiastic group of cloud news seekers? You've come to the right place! Send us an email or hit us up on our slack channel for more info.  Follow Up 00:46 OpenAI's Newest Possible Threat: Ex-CTO Murati Apologies listeners – paywall article.  Given the recent departure of Ex-CTO Mira Murati from OpenAI, we speculated that she might be starting something new…and the rumors are rumorin'.  Rumors have been running wild since her last day on October 4th, with several people reporting that there has been a lot of churn.  Speculation is that Murati may join former Open AI VP Bret Zoph at his new startup.   It may be easy to steal some people, as the research organization at Open AI is reportedly in upheaval after Liam Fedus’s promotion to lead post-training – several researchers have asked to switch teams.  In addition, Ilya Sutskever, an Open AI co-founder and former chief scientist, also has a new startup.   We'll definitely be keeping an eye on this particular soap opera.  2:00 Jonathan – “I kind wonder what will these other startups bring that’s different than what OpenAI are doing or Anthropic or anybody else. mean, they’re all going to be taking the same training data sets because that’s what’s available. It’s not like they’re going to invent some data from somewhere else and have an edge. I mean, I guess they could do different things like be mindful about licensing.” General News 4:41 Introducing New 48vCPU and 60vCPU Optimized Premium Droplets on DigitalOcean Those raindrops are getting pretty heavy as Digital Ocean announces their new 48vCPU Memory and storage optimized premium droplets, and 60vcpu general purpose and CPU optimized premium droplets.  Droplets are DO's Linux-based virtual machines.   Premium Optimized Droplets are dedicated CPU instances with access to the full hyperthread, as well as 10GBps of outbound data transfer. The 48vCPU boxes have 384GB of memory, and the 60vCPU boxes have 160gb. 6:02 Justin – “I’ve been watchi

From our Sponsors at SimmerGo to TeamSimmer and use the coupon code DEVIATE for 10% on individual course purchases.The Technical Marketing Handbook provides a comprehensive journey through technical marketing principles.A new course is out now! Chrome DevTools for Digital MarketersLatest content from Juliana & SimoArticle: GA4 to Piwik PRO Using Server-side Google Tag Manager by Simo AhavaArticle: Unlocking Real-Time Insights: How does Piwik PRO's Real-Time Dashboarding Feature work? by Juliana JacksonAlso mentioned in the EpisodeKick Point Playbook content consumption tracking recipe from DanaKick Point Playbook Newsletter - The HuddleDana's LinkedIn Learning CoursesGoogle Developers AcademyConnect with Dana DiTomasoDana's LinkedinKick Point Playbook website This podcast is brought to you by Juliana Jackson and Simo Ahava. Intro jingle by Jason Packer and Josh Silverbauer.

Ever wonder how to drive product success when you don't have direct authority over your teams?  In this episode, host Rebecca Kalogeris chats with Leah Zillner, a product manager at Intellum, about the wild ride that is product management. Leah shares her story of transitioning from program management to product, and how Pragmatic Institute's courses helped her navigate the journey. From building market insights through client feedback to using tools like UserPilot, Jira, and BigQuery, Leah has tips that will level up your PM game. She also discusses the internal dynamics of product management, where trust and communication are key (especially when you can't just tell people what to do). Leah talks candidly about learning from mistakes, ditching perfectionism, and building a supportive team culture.  Ready to pick up some insider secrets on how to build relationships, communicate better, and juggle the challenges of product management? This episode has you covered!  For detailed takeaways, show notes, and more, visit: www.pragmaticinstitute.com/resources/podcasts  Pragmatic Institute is the global leader in Product, Data, and Design training and certification programs for working professionals. Learn more at www.pragmaticinstitute.com. 

Simba Khadder is the Founder & CEO of Featureform. He started his ML career in recommender systems where he architected a multi-modal personalization engine that powered 100s of millions of user's experiences. Unpacking 3 Types of Feature Stores // MLOps Podcast #265 with Simba Khadder, Founder & CEO of Featureform. // Abstract Simba dives into how feature stores have evolved and how they now intersect with vector stores, especially in the world of machine learning and LLMs. He breaks down what embeddings are, how they power recommender systems, and why personalization is key to improving LLM prompts. Simba also sheds light on the difference between feature and vector stores, explaining how each plays its part in making ML workflows smoother. Plus, we get into the latest challenges and cool innovations happening in MLOps. // Bio Simba Khadder is the Founder & CEO of Featureform. After leaving Google, Simba founded his first company, TritonML. His startup grew quickly and Simba and his team built ML infrastructure that handled over 100M monthly active users. He instilled his learnings into Featureform's virtual feature store. Featureform turns your existing infrastructure into a Feature Store. He's also an avid surfer, a mixed martial artist, a published astrophysicist for his work on finding Planet 9, and he ran the SF marathon in basketball shoes. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: featureform.comBigQuery Feature Store // Nicolas Mauti // MLOps Podcast #255: https://www.youtube.com/watch?v=NtDKbGyRHXQ&ab_channel=MLOps.community --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Simba on LinkedIn: https://www.linkedin.com/in/simba-k/ Timestamps: [00:00] Simba's preferred coffee [00:08] Takeaways [02:01] Coining the term 'Embedding' [07:10] Dual Tower Recommender System [10:06] Complexity vs Reliability in AI [12:39] Vector Stores and Feature Stores [17:56] Value of Data Scientists [20:27] Scalability vs Quick Solutions [23:07] MLOps vs LLMOps Debate [24:12] Feature Stores' current landscape [32:02] ML lifecycle challenges and tools [36:16] Feature Stores bundling impact [42:13] Feature Stores and BigQuery [47:42] Virtual vs Literal Feature Store [50:13] Hadoop Community Challenges [52:46] LLM data lifecycle challenges [56:30] Personalization in prompting usage [59:09] Contextualizing company variables [1:03:10] DSPy framework adoption insights [1:05:25] Wrap up

What makes MotherDuck and DuckDB a game-changer for data analytics? Join us as we sit down with Jacob Matson, a renowned expert in SQL Server, dbt, and Excel, who recently became a developer advocate at MotherDuck. During this episode, Jacob shares his compelling journey to MotherDuck, driven by his frequent use of DuckDB for solving data challenges. We explore the unique attributes of DuckDB, comparing it to SQLite for analytics, and uncover its architectural benefits, such as utilizing multi-core machines for parallel query execution. Jacob also sheds light on how MotherDuck is pushing the envelope with their innovative concept of multiplayer analytics.Our discussion takes a deep dive into MotherDuck's innovative tenancy model and how it impacts database workloads, highlighting the use of DuckDB format in Wasm for enhanced data visualization. Jacob explains how this approach offers significant compression and faster query performance, making data visualization more interactive. We also touch on the potential and limitations of replacing traditional BI tools with Mosaic, and where MotherDuck stands in the modern data stack landscape, especially for organizations that don't require the scale of BigQuery or Snowflake. Plus, get a sneak peek into the upcoming Small Data Conference in San Francisco on September 23rd, where we'll explore how small data solutions can address significant problems without relying on big data. Don't miss this episode packed with insights on DuckDB and MotherDuck innovations!Small Data SF Signup  Discount Code: MATSON100What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.

Google Cloud Data InnovationsThe Big Themes:Integration of unstructured data with AI: Google Cloud is shifting how enterprises leverage their data by integrating unstructured data (which makes up 85-90% of all data) with structured data through its BigQuery multimodal data foundation. This integration allows for a more comprehensive data landscape where AI models can seamlessly access and analyze both types of data. This approach addresses the limitations of traditional data systems and unlocks new potential for AI-driven analytics.The role of partners in maximizing AI and data value: Google Cloud's service partners implement solutions and bring industry best practices to customer environments, while independent software vendors (ISVs) build applications that leverage Google Cloud's data and AI tools. Programs like the Google Cloud Ready (GCR) initiative streamline integrations.Integration challenge: The challenge for organizations lies in connecting disparate data sources, such as operational data from systems like SAP and CRM data from Salesforce, with analytics tools to enable real-time decision-making. Google Cloud addresses this by developing connectors, such as Cortex.The Big Quote: “We are coming to the Third Age in data, which is going to divide data systems. It's not just about having lots of one data type… it's having the broadest possible set of data signals you can bring together. That idea of wide data systems means combining all of your data signals, structured and unstructured, into one unified system."

The newest tech SEO conference is coming to Raleigh, North Carolina, this fall! Guests JR Oakes, Patrick Stox, and Matthew Kay have come together to create an all-new SEO experience, Tech SEO Connect, coming to Raleigh on October 17th & 18th. Don't miss the heavy list of speakers covering core web vitals, Ahrefs Lang, data warehousing, BigQuery, machine learning, and more. In this show, we discuss the origin of Tech SEO Connect with the founders themselves. Learn what makes Tech SEO Connect different from the rest with a diverse content lineup made by technical SEOs for technical SEOs. Get your tickets and mark your calendar as we are all gearing up for the inaugural Tech SEO Connect conference coming this fall. See you there! Key Segments: [00:01:00] Introducing Panelists [00:03:04] The All New TechSEOConnect Conference  [00:07:18] Who is TechSEOConnect Designed For?  [00:12:29] Speakers on the Ballot for Tech SEO Connect [00:13:40] EDGE of the Web Title Sponsor: Site Strategics [00:21:40] Featured Sponsors to Expect at the Conference [00:23:48] What Challenges Arise While Planning an Industry Conference?  [00:24:00] EDGE of The Web Sponsor: Wix   [00:25:47] Unexpected Benefits to Planning Tech SEO Connect  [00:28:06] Tech SEO Connect's Venue Follow Our Guests JR Oakes JR Oakes GitHub Patrick Stox  Matthew Kay  TechSEOConnect Resources:  Tech SEO Connect (Tickets Here)

Nicolas Mauti is an MLOps Engineer from Lyon (France), Working at Malt. BigQuery Feature Store // MLOps Podcast #255 with Nicolas Mauti, Lead MLOps at Malt. // Abstract Need a feature store for your AI/ML applications but overwhelmed by the multitude of options? Think again. In this talk, Nicolas shares how they solved this issue at Malt by leveraging the tools they already had in place. From ingestion to training, Nicolas provides insights on how to transform BigQuery into an effective feature management system. We cover how Nicolas' team designed their feature tables and addressed challenges such as monitoring, alerting, data quality, point-in-time lookups, and backfilling. If you're looking for a simpler way to manage your features without the overhead of additional software, this talk is for you. Discover how BigQuery can handle it all! // Bio Nicolas Mauti is the go-to guy for all things related to MLOps at Malt. With a knack for turning complex problems into streamlined solutions and over a decade of experience in code, data, and ops, he is a driving force in developing and deploying machine learning models that actually work in production. When he's not busy optimizing AI workflows, you can find him sharing his knowledge at the university. Whether it's cracking a tough data challenge or cracking a joke, Nicolas knows how to keep things interesting. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Nicolas' Medium - https://medium.com/@nmauti Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Nicolas on LinkedIn: https://www.linkedin.com/in/nicolasmauti/?locale=en_US Timestamps: [00:00] Nicolas' preferred beverage [00:35] Takeaways [02:25] Please like, share, leave a review, and subscribe to our MLOps channels! [02:57] BigQuery end goal [05:00] BigQuery pain points [10:14] BigQuery vs Feature Stores [12:54] Freelancing Rate Matching issues [16:43] Post-implementation pain points [19:39] Feature Request Process [20:45] Feature Naming Consistency [23:42] Feature Usage Analysis [26:59] Anomaly detection in data [28:25] Continuous Model Retraining Process [30:26] Model misbehavior detection [33:01] Handling model latency issues [36:28] Accuracy vs The Business [38:59] BigQuery cist-benefit analysis [42:06] Feature stores cost savings [44:09] When not to use BigQuery [46:20] Real-time vs Batch Processing [49:11] Register for the Data Engineering for AI/ML Conference now! [50:14] Wrap up

Highlights from this week's conversation include:David's Background and Career (0:49)Econometrics Work at UPS (3:14)Challenges with Time Series Data and Tools (7:15)Working at Google Cloud (11:28)BigQuery's Significance (13:51)Comparison of Data Warehouse Products (17:23)Learning different cloud platforms (20:17)Coherence in GCP (23:04)Observability and data analysis (32:44)Support for Iceberg format in BigQuery (36:31)AI in Observability (40:25)AI's Role in Observability (43:39)AI and Mental Models (46:04)Final thoughts and takeaways (48:32)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.