Podcasts about BigQuery

In today's Cloud Wars Minute, I look at why two of the fastest-growing Cloud Wars companies are joining forces around data, AI, and industry solutions. Highlights 00:03 — When heavy weather rolls in, it's good to have friends around. It's good to have partnerships, and I don't think the AI Revolution is so much heavy weather, but that depends on how well prepared businesses are to take advantage of it, how aggressively, how thoughtfully they're moving into this AI Revolution. 00:41 — It's interesting, Google Cloud and Palantir, on the Cloud Wars Top 10, these are the two fastest-growing companies. Google Cloud grew 63%; Palantir grew 70%. Palantir's commercial business grew 133% in the first quarter, so they've got enormous momentum. 01:30 — The Palantir Foundry platform for enterprise data management is now available on Google Cloud infrastructure and on the Google Cloud Marketplace. Google Cloud and Palantir have built connectors between Foundry and Google Cloud's BigQuery, allowing data from those platforms and others to be pulled together for businesses to analyze. 02:09 — Not just the technical integrations, which have to happen, but also this desire for these two companies to say, "We're going to jointly develop industry-specific solutions around data and AI for vertical markets." The first two they picked are retail and financial services. 03:15 — This is a dream partnership, I think. And it's also probably an example of how, with the enormity of the prospects of what can happen here in the AI Revolution, we're going to see more of the Cloud Wars Top 10 companies form these sorts of wide-ranging partnerships. 04:19 — There's a big emphasis from both of these companies on keeping things open and fully accessible for whichever specific routes customers want to take. We're seeing these inextricably bound connections here through this partnership of data, which is the fuel for AI, helping companies transform into AI-powered enterprises. Visit Cloud Wars for more.

Jordan Tigani helped build BigQuery, then left to bet that most data isn't big. Three years on, agents are proving him right. The MotherDuck CEO joins Tristan Handy on why local-first databases fit the agent era, and what an "agent swarm for data management" looks like. For full show notes and to read the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

What if the data engineering skills you have today become obsolete in five years? In this episode, host Benjamin Wagner sits down with Pranav Motarwar, a data engineer who's witnessed the industry's transformation from traditional ETL to AI-powered pipelines, to explore how AI is fundamentally reshaping data engineering roles, why you need to master both "AI for data" and "data for AI" to stay relevant, and the emerging infrastructure required to handle multimodal data at scale. Whether you're a data engineer wondering about your career longevity or a builder curious about next-gen data stacks, this conversation unpacks the skills you'll need, the tools defining 2026, and why data engineers aren't disappearing - they're just evolving faster than ever.

Full show notes and transcript  - https://bit.ly/google-agentic-eraWatch on YouTube - https://youtu.be/eamMBmm6oTU-----Episode Summary:Dara and Matthew open with a breaking-news bulletin on Anthropic's newly released Fable, the consumer sibling to Mythos, covering its safety off-ramp to Opus 4.8, its pricing, and the looming switch from subscription to usage-based access. The main episode is a deep dive on Google Cloud Next '26 and I/O '26, unpacking the Gemini Enterprise Agent Platform, Gemini 3.5 Flash, Omni, Antigravity 2.0, WebMCP, and the shift to generative AI search. The thread running through it all: agents are the headline, but governance and a solid semantic layer are the subplot that makes them actually useful.-----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!

[Expertpanelen] Avsnitt 160 med Johan Strand, senior digital analyst och partner på Ctrl Digital, om hur vi som marknadsförare kan börja prata med vår data och få svar med hjälp av AI, agenter och nya funktioner. Från Googles Ask Advisor, Conversational Analytics och dataagenter i Data Studio. Till möjligheterna med att koppla Claude eller ChatGPT mot olika plattformar via MCP. Samt varför svaren och analyserna du får bara är så bra som din setup och kontext. Du får dessutom höra om: Var han anser att marknadsförare ska börja Hur AI låser upp nya typer kvalitativ analys Nackdelarna med plattformsspecifika agenter Teknisk skuld är största hindret för AI-analys Skapa agenter med Conversational Analytics Varför analys behöver en human-in-the-loop Tips på analyser som AI kan köra schemalagt Du får också höra en lightning round om nyheter kring Meridian Studio, Google Tag Manager, Google Ads Data Manager och Microsoft Clarity. Om gästen Johan Strand är senior digital analyst och partner på Ctrl Digital, en av Sveriges ledande analytics-byråer. Han är otroligt vass på Google Analytics, BigQuery och att bygga datastrukturer som skapar affärsnytta. Som återkommande expert i poddens nyhetspanel delar Johan regelbundet sina analyser av de viktigaste förändringarna inom digital analys, spårning och datainsamling. Johan är också en av arrangörerna av MeasureCamp Malmö. Tidsstämplar [00:02:25] Plattformsagenter från Google och Meta. Googles Ask Advisor och Metas AI Business Assistant, plattformarnas inbyggda agenter, vad de är bra på och var de brister. [00:04:20] Data Studio och Conversational Analytics. Data Studio är tillbaka och Conversational Analytics har blivit gratis. Johan förklarar hur du bygger en dataagent med egen kontext och guardrails. [00:10:15] MCP:er och jämförelsen med agenterna. Rollen som MCP:er spelar när de kopplas in i AI-verktyg som Claude och ChatGPT, och hur det skiljer sig från de inbyggda agenterna. [00:17:35] Rapportering vs analys och AI:s styrkor. Varför rapportering är en tryggare startpunkt än analys, och var AI briljerar: från snabba kvantitativa svar till kvalitativ data och verifiering. [00:27:10] För- och nackdelar samt användningsområden. Plattformsagenter, dataagenter och MCP-kopplingar ställs mot varandra, plus Johans bästa användningsområden och varför teknisk skuld bromsar. [00:33:33] Komma igång med AI inom analysarbetet. Hur långt de flesta marknadsteam har kommit, schemalagd anomaly detection, och Johans bästa tips och råd. [00:38:37] Lightning round: Meridian Studio och MMM. Googles Meridian Studio och varför marketing mix modeling gör comeback nu när last click-attributionen blir allt mer opålitlig. [00:44:02] Google Tag Managers största uppdatering. Nytt UI, containrar som blir Google-taggar och en ny visuell eventbyggare. Och vad det här innebär för användare. [00:47:40] Google Ads Data Manager och Microsoft Clarity. Google gör det enklare att skicka data mellan sina plattformar, och Microsoft Clarity tar en allt större plats i analys-stacken. Länkar Johan Strand på LinkedInCtrl Digital (webbsida) Meet Ask Advisor, your new AI-powered collaborator – Google (artikel)Want to improve ad results? Ask Meta AI business assistant – Meta (artikel)Conversational Analytics in Data Studio overview – Google (dokumentation)Data Studio returns as new home for Data Cloud assets – Google (artikel) Introducing Meta Ads AI Connectors: Manage Your Meta Ads From the AI Tools You Already Use – Meta (artikel)Use AI-powered skills to run ads on TikTok – TikTok (webbsida) Lightning round:Meridian StudioGoogle Ads Data ManagerGoogle Tag Manager-uppdateringarMicrosoft Clarity Veckans partners Huvudpartner: DigitalentaPartnernätverket: Paloma, Check och Klingit Se alla partners här tonyhammarlund.io/partners

Most B2B businesses are spending half a million pounds or more a year on a go-to-market model that doesn't work. Not because the people running it aren't capable, but because the model itself is broken. Tools that don't talk to each other. Teams whose job is to operate those tools. Outbound sequences that get ignored. And an ROI that is, almost universally, terrible.In this episode, Nigel Maine breaks down the structural cost of fragmented GTM, explains why serious B2B buyers do not respond to interruption-based selling, and shows — with live data — what a broadcast-driven commercial infrastructure actually produces when you stop chasing and start being visible. He also reveals something that happened this week that is one of the most commercially significant developments in B2B AI right now: a 1.53 million word IP corpus, indexed and queryable in BigQuery, producing show scripts, LinkedIn posts, and investor communications indistinguishable from what the founder would have written himself.If you run a B2B business with a complex sale, senior buyers, and a decision-making cycle that takes months — this is for you. Watch to the end for the data.What this episode coversThe real cost of fragmented GTM — tools, headcount, agencies, and ad spendWhy serious B2B buyers research anonymously and don't respond to outboundThe Mere Exposure Effect and why consistency builds purchase-ready trustWhat sX Live actually is and why broadcast is not the same as video or webinars90-day data: 1,668 PDF downloads, 35% email open rate, 7.8% LinkedIn engagement — all organicHow Claude wrote this show script from a 1.53 million word indexed IP corpusThe difference between using AI as a chat tool and deploying AI as a component of a commercial operating systemWhat a queryable BigQuery telemetry layer gives you that no CRM canWho this model is for — and who it isn'tWho should watchB2B founders, CEOs, MDs, and commercial directors who are questioning their current GTM spend and want to understand whether a broadcast-driven, AI-augmented infrastructure could replace what they're currently paying for.Take the next stepDownload the GTM Reset, GTM Landscape, or GTM Architecture Audit PDFs at salesxchange.co.uk — or email nigel@salesxchange.co.uk to talk about what this looks like in your business.

AI agents sound exciting. But my conversation with A. Ravi M., CIO at Box at Google Cloud Next '26 on The Ravit Show was not about excitement.It was about risk. We are moving from AI that answers to AI that acts. And that shift introduces a completely new set of challenges. Not just accuracy, but control, access, and accountability. Ravi pointed out that most enterprises are not struggling with AI capability. They are struggling with governance. Who has access to what data, what an agent is allowed to do, and how you track those actions. Those gaps become very real once agents start operating on sensitive enterprise content.And that is where security needs to evolve. It is no longer enough to protect data at rest. You have to think about how AI agents interact with that data in real time, and what guardrails are in place when they take action.The partnership with Google Cloud plays a big role here. With platforms like Vertex AI and BigQuery, the focus is not just on building agents, but on building them with the right controls and visibility from day one.The biggest takeaway for me was simple. If you are a CIO thinking about AI agents, do not start with deployment. Start with trust. Because without that, none of this scales.#data #ai #box #security #googlecloudnext #api #google #theravitshow

Most B2B companies are invisible to 95% of their total addressable market. Not because their product is weak — but because they have been handed a consumer-grade marketing playbook and told to get on with it. Same software, same tactics, same results. That ends here.In Episode 10 of the GTM Reset, Nigel Maine breaks down why the broadcast infrastructure model exists, what it actually does, and how sX Reach — the first module of the sX Operating System — puts 600 unique posts a month into your market, on repeat, without a team to run it. He also covers the telemetry layer: every send, every click, every download, fed into BigQuery and reported through Claude in plain English.Watch this if you are done listening to marketers tell you social media doesn't work. It works. You just haven't been doing it at scale.Watch the full show Episode #10: https://salesxchange.co.uk/live-04/item/from-invisible-to-everywhere?utm_source=podcast&utm_medium=audio&utm_campaign=gtm_reset_2026&utm_content=ep10What this episode covers- Why Andreessen Horowitz's "systems of intelligence" argument validates what sX OS already built- The two types of fake operating systems: DIY drag-and-drop platforms and ring-binder playbooks- The 20/30/50% business failure data — and why copying everyone else guarantees identical results- How to visualise your total addressable market across unknown and known audiences- Why social media platforms exist to facilitate broadcasting — and what that means for B2B- How one track of 30 posts, running across 20 profiles, generates 600 posts a month on repeat- The multiplication effect: one live stream becomes video, transcript, clips, shorts, and podcast- 66,500 views and impressions, 70+ hours of watch time, 1,600 downloads — one person, since March- How every data stream feeds into BigQuery so the CEO can ask Claude and get an answer in seconds- sX Reach in detail: social post construction, email via API, coordinated LinkedIn banner distributionWho should listenB2B founders, CEOs, and revenue leaders who are spending on people or platforms and not seeing results proportional to the investment. If your average sales cycle is measured in months, your total addressable market is larger than your pipeline, and social media feels like a waste of time — this is the show.Take the next stepDownload the GTM Revenue Reset or book a GTM Audit Meeting at the links below. Episode 11 covers sX Live — what it means to broadcast your own weekly show and build the trust that makes your TAM want to buy.Resources and linksDownload our Three-Part GTM Reset Series PDFshttps://salesxchange.co.uk/gtm-ceo?utm_source=podcast&utm_medium=audio&utm_campaign=gtm_reset_2026&utm_content=ep10Request Your GTM Audit Meetinghttps://salesxchange.co.uk/gtm-ceo/gtm-audit?view=article&id=301:gtmos-audit-questionnaire&catid=52&utm_source=podcast&utm_medium=audio&utm_campaign=gtm_reset_2026&utm_content=ep10

In this special episode of the Independent Dealer Podcast, recorded live on location at Buy Here Pay Here United 2026, Jeff Watson and Luke Godwin flip the script with a first-of-its-kind vendor panel. Instead of the traditional dealer open forum, six of the industry's top service providers take the stage to share what they see from their side of the glass — the blind spots, pain points, and opportunities that dealers are missing right now. Featuring Steve Levine (Ignite Dealer Compliance Group), Mike Downey (Auto Master Systems), Bill Neylan (Tax Max), Jason Gosnell (Buckeye Risk Services), Ariad Sommer (Ituran USA), and Terry MacCauley (Big Time Advertising), this panel pulls back the curtain on AI, automation, compliance, disaster planning, parts sourcing, and where the BHPH industry is headed next.What You'll Learn:-Why AI in your dealership can be a compliance time bomb — and why every store needs a written AI policy-How dealers are "doing more with less" using data warehouses (BigQuery, Snowflake) instead of dumping PII into ChatGPT-The heated debate over AI replacing employees — and why some 40-year dealers refuse to use it at all-Why most dealers have NO contingency plan to operate when an ice storm, hurricane, or outage shuts the doors during tax season-The backup systems every dealer needs: power, internet (Starlink), VOIP phones, and remote-ready staff-How starter interrupt and GPS integration can collect a late payment automatically — without you ever picking up the phone-Why you're probably paying for features your current vendors already offer but never turned on-How automotive recyclers became the "Amazon fulfillment center" of parts — and how it's lowering recon costs-The massive shift in search: customers now treat Google like ChatGPT, and organic traffic is down 20–30%-How to get your dealership to show up in AI Overviews (and why the top 10% of your website is everything)-What vendors wish dealers would do: communicate your pain points, stop ghosting, and never cancel over cost alone-Where six industry insiders see Buy Here Pay Here heading over the next 10 yearsIf you're a buy here pay here or independent dealer trying to navigate AI, automation, compliance, and an industry that's changing faster than ever, this vendor panel is packed with insider perspective you won't hear anywhere else. These are the people who touch hundreds of dealers every month — and they're telling you exactly what's working, what's coming, and what's quietly costing you money.Support the businesses that support the podcast:Buckeye Risk Services - Reinsurance and wealth strategies for independent dealers. https://theindependentdealer.com/buckeyeBlytz - BHPH payment processing with fast funding and text-to-pay. https://theindependentdealer.com/blytzpayIturan GPS - Asset protection and customer management for BHPH and retail dealers. https://theindependentdealer.com/ituranFollow & Connect:Website: www.theindependentdealer.comFacebook Group: @independentautogroupLuke Godwin: @lukegodwinJeff Watson: /sendtojeffwLike, subscribe, and share this with a dealer who needs to hear it.

Parce que… c'est l'épisode 0x2FD! Shameless plug 3 au 5 juin 2026 - SSTIC 2026 24 et 25 juin 2026 - Troopers 26 et 27 juin 2026 - leHACK 19 septembre 2026 - Bsides Montréal 1 au 3 décembre 2026 - Forum INCYBER - Canada 2026 24 et 25 février 2027 - SéQCure 2027 Description Dans cet épisode spécial, Nicolas Bédard revient sur sa participation à Google Next 2026, son quatrième événement du genre, mais le premier qu'il vivait en tant qu'employé de Palo Alto plutôt que de Google. Il y présente les quatre intégrations majeures que Palo Alto a lancées en partenariat avec Google, dans un contexte où l'intelligence artificielle agentielle se déploie à grande vitesse — souvent sans encadrement de sécurité adéquat. Le contexte : la plateforme Gemini Enterprise se réorganise Avant d'aborder les intégrations, Nicolas explique les changements de nomenclature chez Google. Gemini Enterprise est désormais divisé en deux volets : Gemini Enterprise Apps : l'interface utilisateur permettant d'accéder aux agents, aux connecteurs de données (SharePoint, Outlook, etc.) et aux outils IA. Gemini Enterprise AI Platform : la couche cloud sous-jacente, qui remplace l'ancienne plateforme Vertex AI. Cette restructuration simplifie la compréhension de l'écosystème : tout ce qui touche à l'IA en entreprise chez Google s'appelle désormais Gemini Enterprise. Intégration 1 — Prisma AIRS dans l'Agent Gateway La première et probablement la plus stratégique des intégrations concerne Agent Gateway, une nouvelle fonction au cœur d'Agent Cloud, la plateforme Google pour exécuter des agents IA. Agent Gateway agit comme un point d'insertion au sein des load balancers internes : il permet d'injecter des fonctions de sécurité ou d'autres capacités dans les flux de communication entre agents, entre un agent et un serveur MCP, ou entre un utilisateur et son agent. Palo Alto a annoncé l'intégration de son AI Runtime de Prisma AIRS directement dans ce gateway. L'idée est de centraliser la sécurité plutôt que de la déléguer à chaque développeur. Concrètement, cela signifie que les garde-fous — validation des comportements, prévention des fuites de données, protection contre les abus — s'appliquent automatiquement à tous les agents, sans que les équipes de développement aient besoin d'expertise en cybersécurité. Agent Gateway s'articule autour de trois piliers : l'identité, le runtime (pare-feu IA) et l'observabilité. Pour l'instant, seuls les deux premiers sont ouverts aux partenaires tiers comme Palo Alto. Cette approche répond directement à la préoccupation numéro un des équipes de sécurité en entreprise : le Shadow AI, soit l'utilisation non contrôlée d'outils IA par des employés ou des développeurs, qui expose l'organisation à des risques importants. Intégration 2 — Le scan de modèles open source via Gemini Enterprise Apps La deuxième intégration adresse un risque souvent sous-estimé : l'utilisation de modèles IA provenant de plateformes communautaires comme Hugging Face. Si les grands modèles commerciaux (Google, Anthropic, OpenAI, Mistral) offrent des garanties relatives à leur provenance, les modèles open source sont publiés par n'importe qui, sans vérification systématique. Ils peuvent contenir des vulnérabilités cachées, des kill switches, du code malveillant dissimulé dans l'enveloppe du fichier (notamment via des fichiers pickle), ou avoir été entraînés sur des données douteuses. Palo Alto a lancé un agent de scan de modèles directement accessible depuis Gemini Enterprise Apps. Intégré au cycle de développement logiciel (SDLC), cet agent permet à un développeur de soumettre un modèle hébergé sur Hugging Face ou dans un registre interne pour vérification avant déploiement — sans avoir à sortir de son environnement de travail habituel. Nicolas précise que cet agent fonctionne dans le tenant du client, ce qui garantit que les données restent dans l'infrastructure de l'entreprise. Intégration 3 — Wildfire et l'analyse de malwares dans les flux IA La troisième intégration s'inscrit dans une approche plus classique, mais essentielle : la détection de malwares dans les fichiers transitant par des agents IA. Google utilisait déjà la technologie de pare-feu de Palo Alto pour son Cloud NGFW. Ce qui est nouveau à Google Next, c'est l'ajout de Wildfire, le moteur de sandboxing de Palo Alto, sous la forme d'un service géré appelé Advance Malware Sandboxing. Concrètement : lorsqu'un utilisateur envoie un fichier via un agent Gemini Enterprise — vers un dépôt documentaire, par exemple — ce fichier est intercepté, analysé dans un environnement isolé, puis validé avant d'être stocké. Cela protège les autres utilisateurs ou agents qui pourraient accéder à ce fichier ultérieurement. L'enjeu est d'autant plus grand que les malwares générés par IA sont désormais créés on the fly, spécifiquement pour une cible, ce qui rend les approches basées sur des signatures connues insuffisantes. Intégration 4 — Le pare-feu dans l'Application Design Center La quatrième intégration touche à l'expérience des développeurs. Google a ouvert son Application Design Center (ADC) aux partenaires tiers. L'ADC est un outil visuel dans la console cloud qui permet d'assembler des services Google (Cloud Run, Pub/Sub, BigQuery, etc.) pour créer des applications. Palo Alto a travaillé avec Google pour permettre l'insertion native d'un pare-feu dans ces assemblages. Un développeur qui crée une architecture dans l'ADC peut maintenant ajouter un gabarit Palo Alto d'un clic. Une fois la configuration validée, l'outil génère automatiquement le code Terraform correspondant, incluant les load balancers et le pare-feu. L'objectif est de démocratiser la sécurité réseau en la rendant accessible à des développeurs qui ne maîtrisent pas nécessairement les subtilités des pare-feux d'infrastructure. Collaborateurs Nicolas-Loïc Fortin Nicolas Bédard Crédits Montage par Intrasecure inc Locaux réels par Nicolas Bédard

Are AI agents silently draining your cloud data budget? With the rise of consumption-based pricing and autonomous AI queries, data teams are facing a perfect storm of skyrocketing costs and operational chaos. In this episode, I sit down with Sanjay Agrawal, CEO and Co-founder of Revefi, to discuss the intersection of data engineering, cloud warehouse optimization, and FinOps in the age of AI.We chat about how legacy on-prem habits are bankrupting modern data platforms, why query optimization is more about ROI than just speed, and how AI agents are changing the landscape of data consumption. Sanjay shares his deep expertise from building world-class databases at Microsoft and ThoughtSpot, revealing how to automate cost management and performance tuning for Snowflake, Databricks, and BigQuery.Key Topics:The evolution of cloud data warehouse pricing and why it breaks traditional budgets.How AI agents are causing massive, unpredictable spikes in compute spend.Real-world horror stories of ""lift and shift"" cloud migrations.Why database benchmarks focus on speed but ignore the actual ROI of data.The future of open table formats (Iceberg) and multi-engine routing.

Building Repeatables in Claude: Skills, CLI vs MCP and Token Discipline | Go With The Flow Claude Skills, CLI vs MCP and Token Discipline with Ritu Java | Seller Sessions SEO Description Ritu Java and Danny McMillan on building agentic skills, choosing CLI over MCP, plan mode discipline and the short window to ship before token costs reset. Episode Summary Week 4 of the month, Go With The Flow, and Ritu Java is back from her travels. The world has shipped fast since the last episode: Codex 5.5, Claude 4.7, an Amazon Ads MCP and a fresh round of panic over the rumoured removal of Claude Code from the $20 plan (it was a 2% AB test, not a rollout). Ritu and Danny use the noise to make a sharper point: this is the moment to stop chasing models and start building repeatable systems on the platform you have already chosen. Ritu walks through the three eras of PPC Ninja's automation stack. Apps Script bulk file generators three years ago, Netlify hosted UI apps last year, and now agentic skills that her team chats with in plain English to produce upload ready Amazon bulk files. The same shift applies to data: BigQuery accessed through the Google Cloud CLI rather than through MCP, because CLI is leaner on tokens and works better when the job is heavy on data rather than tool surface. Danny mirrors the move with his event-ops CLI for WordPress, WooCommerce, Stripe and FooEvents reconciliation, and his four tier ExtractFlow cascade (HTTP, headless, stealth, agentic) that bypasses the limits of any single browser tool. The second half is a discipline talk. Plan mode every time. Push back on the first plan because Claude over engineers by default. 30% of your time on workflow scaffolding so the other 70% can be real building. The 21 day Claude rule: when a shiny new tool fires the dopamine, wait 21 days before refactoring around it. Left brain tasks (counting, SQL, deterministic logic) belong in scripts. Right brain tasks (judgment, creativity, hypotheses) belong in the model. Mix them inside a single skill. Skills are micro pieces of your workflow, not magic, and Claude can write them for you from an existing SOP. Key Topics The three eras of PPC Ninja automation: Apps Script, Netlify UI apps, agentic skills CLI vs MCP: when to choose each and why CLI is more token efficient for data heavy work Token economics, the rumoured $20 plan change and why it was a 2% AB test The short window before subsidised tokens get repriced Plan mode discipline and the "push back on plan one" rule Danny's 30 / 70 framework: workflow scaffolding vs building The 21 day Claude rule for resisting tool churn Left brain vs right brain task design inside a single skill The PPC Ninja "5 Whys" skill: deterministic SQL plus non deterministic hypotheses Claude.md, Gemini.md, Skills.yaml and the emerging Agents.md standard Skills for beginners: let Claude write them from your SOP Skill cascading: research, article, LinkedIn post, tweets, slide deck in one chain Timestamps [00:01] Welcome back, Week 4 Go With The Flow, Ritu returns from travels [00:17] Codex 5.5, Claude 4.7 and the "no one is writing code anymore" reality [02:01] Ritu on the three eras of PPC Ninja automation [02:42] Era 1: Apps Script bulk file generators in Google Sheets [03:46] Era 2: Netlify hosted UI apps with input fields [04:48] Era 3: Agentic skills, the bulk file skill trained on Amazon templates [06:22] Cloud talking to BigQuery through the Google Cloud CLI [07:00] Danny: what is a CLI and why it matters for token use [08:00] Amazon Advertising MCP vs CLI based access to the same data [09:33] WordPress horrible to drive via MCP, easy via CLI [10:00] Danny's event-ops CLI: tickets, food tickets, WooCommerce, Stripe reconciliation [12:13] ExtractFlow four tier cascade: soft, medium, stealth, agentic [13:46] Why CLI for the heavy stuff, MCP for the soft touch [14:13] AWS CLI: chat to Claude, push HTML blog posts live in two minutes [15:33] The overwhelm problem and the 5,000costbehindthe5,000costbehindthe100 plan [17:35] The $20 plan rumour: it was a 2% AB test, not a rollout [19:38] Build repeatables, not one offs [20:38] Danny: pick a platform and stop chasing benchmarks [21:16] The 21 day Claude rule for new tools [22:16] Plan mode every time, push back on plan one, get the second plan [23:02] Why am I building it, who is it for, what am I building [23:30] The 30 / 70 split: workflow scaffolding vs real building [25:13] Why long six to fourteen hour Claude runs are usually inefficiency [27:12] Compounding 1% a day across a year [27:47] "I build the things that build things" [28:00] Architecture vs apps: filling the gaps between A and B [29:06] Left brain vs right brain task design [30:01] Why throwing 80/20 at a sales drop diagnosis fails [31:33] The PPC Ninja 5 Whys skill: deterministic plus non deterministic in one flow [34:32] Claude.md, Gemini.md, skills.yaml and the agents.md standard [40:53] Beginners: let Claude write the skill from your SOP, use the interview pattern [42:39] Skill cascading: URL to research to article to LinkedIn post to tweets to slides [44:42] Mixing deterministic and non deterministic inside a single skill [45:39] Wrap up, signal to noise, who is it for Key Takeaways Pick a platform and stop chasing models. A new model ships every week. Time spent benchmarking is time not building. Double down on Claude (or whichever you chose), use the 21 day rule, and let the ecosystem catch up to the shiny thing in your feed. CLI for heavy work, MCP for soft touch. MCP loads tools and skills into context and burns tokens. CLI uses programs already on your machine. For data heavy jobs (BigQuery, AWS, WordPress at scale), CLI wins. For light cross app workflows, MCP is fine. Build repeatables, not one offs. Subsidised tokens will not last. The 100planreportedlycostsAnthropic100planreportedlycostsAnthropic5,000 to serve. Spend the window building scaffolding that compounds, not 14 hour vibe coding runs. Plan mode every time, then push back. Claude over engineers by default. Generate the plan, then say "you have over engineered this, although I want it elegant, go back and review." Plan two is the one you start from. 30% on workflow, 70% on building. Each new dependency, MCP, skill or repo you add to your workflow compounds across every future project. Stop building only the apps. Build the things that build the apps. Left brain in scripts, right brain in the model. Counting, SQL, deterministic logic belongs in Python the moment you can offload it. Save the model for hypotheses, judgment and creativity. The PPC Ninja 5 Whys skill mixes both inside one flow. Skills are micro pieces, not magic. Take an SOP, ask Claude to interview you with decision panels, and let it write the skill. Then cascade skills together: URL to research to long form article to LinkedIn post to tweets to slide deck. Notable Quotes "Instead of doing one offs, it is time to build repeatables. The more people can learn that skill now, the better it will be, because a year from now you may not have access to the same tokens." Ritu Java "If you see something and it looks sexy and it has sex and sizzle and your dopamine is screaming to go after it, wait 21 days. Either Claude will have it, or someone will have a repo, and you can combine it." Danny McMillan "Always use plan mode. Never accept plan number one. Tell Claude: you have over engineered this, although I want it elegant, go back and review. Then start from plan two." Danny McMillan "I build the things that build things. I build the scaffolding the team needs so they can build on top of it." Danny McMillan "Spend 30% of your time on your workflow and 70% building. The 30% compounds across every project." Danny McMillan "If we just hand six months of ad, organic, ranking and SQP data to Claude with no structure, it is going to mess up. It will give you an 80/20 you are not satisfied with, because it is not equipped to handle that volume without scaffolding." Ritu Java "WordPress is horrible to work with through MCP. It falls over all the time. CLI can be amazing for certain things." Danny McMillan Resources Mentioned PPC Ninja : Ritu's Amazon PPC software and agency, base for the BigQuery + CLI stack discussed Claude Code : Anthropic's CLI for Claude, the primary surface used in the episode Anthropic Claude : Claude 4.7 referenced as the current model OpenAI Codex : Codex 5.5 mentioned as the rival shipping fast Google Gemini CLI : Referenced as a sibling agent surface (Gemini.md) Google BigQuery : PPC Ninja's central data warehouse Google Cloud CLI (gcloud) : The CLI Claude uses to talk to BigQuery Amazon Advertising MCP : Amazon's official MCP server for ads data, referenced as the MCP comparison point AWS CLI : Used by Ritu to publish HTML blog posts to ppcninja.com from a Claude chat Netlify : Hosting layer for PPC Ninja's previous era of UI based apps WordPress and WooCommerce : Backbone of Danny's event-ops CLI FooEvents : Ticketing plugin that lives behind WooCommerce in the event-ops flow Stripe : Source of the card fee variation Danny reconciles via CLI ExtractFlow / CloudExtract : Danny's four tier extraction cascade (HTTP, headless, stealth, agentic). Open repo Playwright : The default browser automation tier inside ExtractFlow Agents.md : Emerging AI agnostic instruction file standard alongside Claude.md and Gemini.md Sequential Thinking MCP : The MCP Danny invokes when asking Claude to step through analysis Hosts Danny McMillan : Host of Seller Sessions, founder of DataBrill, building AI native tooling and CLI based workflows for Amazon sellers. Website: https://sellersessions.com LinkedIn: https://www.linkedin.com/in/dannymcmillan Ritu Java : CEO and co founder of PPC Ninja, Amazon PPC software and agency. Specialises in automation, BigQuery pipelines and agentic workflow design. LinkedIn: https://ca.linkedin.com/in/ritujava Website: https://www.ppcninja.com What's Next Next week: Ritu and Danny pick up routines and the new Claude scheduler. In 8 days: Seller Sessions Live 2026 in London on 9 May. Last week to lock in any final discounts. About Seller Sessions Seller Sessions is the leading podcast for serious Amazon sellers, hosted by Danny McMillan since 2017. Go With The Flow is the weekly automation strand where Danny and Ritu Java work through agentic flows, MCPs, CLIs and skills, in real time, on the same stack their teams ship every week. Episode published: 1 May 2026 Series: Go With The Flow (Week 4 of the month) Keywords: claude skills, claude code, cli vs mcp, mcp model context protocol, claude 4.7, codex 5.5, amazon ppc automation, bigquery cli, agentic workflows, plan mode, token optimisation, claude.md, agents.md, ppc ninja, ritu java, seller sessions podcast, go with the flow

Full show notes and transcript  - https://bit.ly/bq-cost-tamingWatch on YouTube - https://youtu.be/2QxXQH6waLk-----Episode Summary:In this episode of The Measure Pod, Dara and Matthew welcome Martin Sahlen, CEO and co-founder of Alvin.ai. Martin shares his journey from studying computer science in Norway to serial entrepreneurship, eventually settling in Tallinn, Estonia, where he founded Alvin. He explains how the company pivoted from data lineage and observability into a focused BigQuery cost optimisation platform that automatically routes queries between billing models to deliver savings, charging a percentage of what it saves. The conversation covers Alvin's transparent, no-lock-in approach, the duality of cost and performance optimisation, and the competitive dynamics of operating alongside Google's own tooling.-----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!

In this special episode of Cloud Wars Live from Google Cloud Next, Bob Evans speaks with Andi Gutmans about Google Cloud's newly announced Agentic Data Cloud and what it means for enterprise customers entering the AI-driven future. Gutmans explains how businesses must rethink data platforms for an era where autonomous agents, not just people, need instant access to trusted enterprise knowledge. The New Data Foundation The Big Themes: The Agentic Data Cloud Is a Reinvention: Google Cloud is not simply rebranding its existing Data Cloud, it is fundamentally redesigning it for the agentic AI era. Gutmans explains that data must evolve from being a passive repository into active business knowledge that agents can reason over. He describes this as moving from a “system of intelligence” to a “system of action.” The newly announced Agentic Data Cloud includes innovations across databases, analytics, storage, and governance so agents can securely access and act on enterprise information. Culture Matters More Than Technology: According to Gutmans, the organizations moving fastest are the ones embracing cultural transformation, not just deploying models on top of old systems. Companies succeeding in the agentic era are rethinking how their data platforms work and how employees engage with AI. Instead of treating agents as copilots, they view every employee as an orchestrator of agents. That mindset shift drives faster ROI because it creates readiness for change and willingness to innovate. Google's Vertical Stack Is a Major Advantage: Gutmans says that Google Cloud is uniquely positioned because it owns the entire stack: AI infrastructure, models, and the data platform itself. This allows what he calls “closed-loop innovation” between models and data systems, where improvements in one directly enhance the other. He says many people underestimate how important that relationship is because model reasoning must evolve alongside the platform serving enterprise data. Products like BigQuery, Spanner, and Gemini benefit from Google's decades of operating at massive scale, including multiple billion-user businesses. The Big Quote: "We're moving from this reactive, agentic experience to agents truly being autonomous, being able to drive outcomes for the business, and that's also now steering how we're thinking about the data cloud." More from Google Cloud: Learn more about what's new in the Agentic Data Cloud and security in the AI era. Visit Cloud Wars for more.

In this episode of Search Off the Record, Martin and Gary turn a simple robots.txt question into a data‑driven deep dive using HTTP Archive, WebPageTest, custom JavaScript metrics, and BigQuery. They explore how millions of real robots.txt files are actually written in 2025–2026, which directives and user‑agents are most common, and what that means for modern crawling and AI bots. Perfect for beginner to mid‑level developers and SEOs, you'll learn how large‑scale web measurement works (HTTP Archive, Chrome UX Report, Web Almanac), and how to turn raw crawl data into actionable SEO insights. Subscribe for more candid conversations about crawling, indexing, and the data behind how Google Search and the web really work. Resources: Web Almanac →  https://almanac.httparchive.org/en/2025/ Robotstxt custom metric for the HTTP Archive →  https://github.com/HTTPArchive/custom-metrics/pull/191 robots.txt parser change → https://github.com/google/robotstxt/commit/4af32e54b715442bb04cd0470e99192f0ffb9792#commitcomment-178586774 Episode transcript → https://goo.gle/sotr108-transcript Listen to more Search Off the Record → https://goo.gle/sotr-yt   Subscribe to Google Search Channel → https://goo.gle/SearchCentral Search Off the Record is a podcast series that takes you behind the scenes of Google Search with the Search Relations team.  #SOTRpodcast #SEO #GoogleSearch Speakers: Martin Splitt, Gary Illyes

Matthieu Colin est Analytics Engineering Manager chez Back Market, la marketplace de produits reconditionnés présente dans 17 pays qui compte plus de 15M de clients.Il va nous parler d'Analytics Engineering : comment construire un data model “trustable” et maîtriser les coûts grâce à un monitoring très fin.On aborde :

In this episode, Yasmeen Ahmad, Managing Director of Product Management for Data & AI Cloud at Google Cloud, reveals why 80–90% of enterprise data is "dark" and untouched — and how Google Cloud is building the tools to finally unlock it. Yasmeen shares how BigQuery's new Knowledge Engine captures the invisible business context that human analysts have always carried in their heads, and why this semantic layer is the real unlock for enterprise AI in 2026. Yasmeen breaks down how enterprises are scaling from 50 to 2,000 autonomous AI agents, why continuous evaluation (not unit testing) is the only way to keep agents trustworthy, and what Google learned from seeing 50% of its own code now written by AI. She also explains why 95% of AI pilots produce zero measurable ROI — and why companies that partner with a platform like Google Cloud see dramatically different results. Plus, her contrarian take on governance: it's not the brake, it's what lets you drive 150 mph into the bend with confidence. Key Topics Covered Why 80–90% of enterprise data is "dark" unstructured data that GenAI can finally unlock How BigQuery's Knowledge Engine captures the invisible business context analysts carry in their heads The semantic layer: why the next big unlock is context, not just more powerful models How enterprises are scaling from 50 to 2,000 autonomous AI agents Intent-driven agentic AI: giving agents outcomes instead of step-by-step instructions Why continuous evaluation is replacing traditional unit testing for AI agents Google's internal AI adoption: 50% of code written by AI, 10% engineering efficiency gains Why 95% of AI pilots produce zero ROI and what changes that outcome AI governance as an accelerator — the "brakes that let you drive 150 mph" framework Why culture and founder mentality matter more than technology budget for AI success Episode Timestamps 00:00 - Introduction and welcome 00:50 - Being Scottish in Silicon Valley and the power of community 03:13 - The career thread: curiosity, pivots, and getting outside your comfort zone 06:20 - What makes data fascinating: the hidden stories inside numbers 07:48 - Why data is the lifeblood of enterprise AI 10:19 - 80–90% of enterprise data is "dark" and untouched 12:59 - What BigQuery actually does (explained simply) 14:50 - The invisible work: knowledge layers and business semantics 17:38 - The agentic AI moment: agents that think, plan, and execute 20:41 - From 50 to 2,000 autonomous agents inside enterprises 22:07 - Why you can't evaluate AI agents like traditional software 25:46 - Signals of AI readiness: Google's 50% AI-written code and Honeywell's 30% efficiency gains 30:21 - Why 95% of AI pilots produce zero ROI 35:37 - Governance as a speed accelerator, not a brake 39:53 - Who's best poised to win: culture over budget 45:59 - Why do you do what you do? Yasmeen's Socials: LinkedIn — https://www.linkedin.com/in/yasmeenahmaduk/ Partner Links Book Enterprise Training — https://www.upscaile.com/ Subscribe to our free newsletter — https://www.theaireport.ai/subscribe Learn more about your ad choices. Visit megaphone.fm/adchoices

Sandrine Kerfers est Head of Data Analytics chez Veepee, la licorne française qui propose des ventes flash sur son site e-commerce. Sandrine va nous parler du plus gros challenge qu'elle a rencontré ces dernières années : adopter une organisation hybride centralisée x décentralisée et une approche Analytics Engineering.On aborde :

Full show notes, transcript and AI chatbot - https://bit.ly/40Zlwd1Watch on YouTube - https://youtu.be/l-tb1uryhg8-----Episode Summary:Dara and Matthew are back with a packed episode. From the Pentagon drama that had Anthropic, OpenAI and Sam Altman making headlines, to Google's latest releases in BigQuery, Flux, Code Wiki and Workspace CLI, there's no shortage of things to unpack. The main event though is a thorough exploration of why both hosts keep coming back to Claude above all else - covering Claude Code, Claude Cowork, scheduled tasks, remote control, plugins and the growing sense that agentic AI has finally crossed over from the CLI world into something anyone can use. Plus, is an OpenAI deep dive episode on the horizon?-----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!

A couple of GSC bugs to report of the top with BigQuery exports not working and an issue with the date selector in crawl stats. In both cases, it's a them thing, not a you thing. If you're running ads through Goolge Ads in the EU you need to confirm if the campaign has political content by the annual deadline of March 31. You can do this via the "campaign settings" link. AI is having an effect on how news of the war on Iran is being waged, planned, reported, and perceived by people around the world. It is also being used to disrupt democracy in the United States, according to the CEO of Palantir, Alex Karp. In an interview with CNBC, Karp claimed his AI will, "... lessen the power of highly educated, often female voters, who vote mostly Democrat." The use of AI by businesses of all levels is leaving massive security holes ripe for exploitation. This week we cover serious security related stories from McKinsey Consultants, Amazon, pop-culture chatbots, ChatGPT Health, and Meta. Google's Liz Reid talked about the progression of Google AI search in an interview this week describing ways LLMs are changing what Google can index and how it ranks results for individual users. Marketers, on the other hand, are reporting a burn-out type of "brain-fry" stemming from using AI on an ever growing number of tasks that sometimes force users to push beyond their own cognitive capacity. Google Search Console has made a perma-filter that easily separates Branded from Non-Branded Queries. For e-com shops, especially larger ones, it's a big deal. Google also offers some tips on the badly misunderstood disavow file. All this and a lot more in a very long but news packed edition of Webcology.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

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 SIMMER COURSE ALERT!  - Data Analysis with R - taught by Arben Kqiku  Latest content from Simo Ahava Run Server-side Google Tag Manager On Localhost Article Latest content from Juliana Jackson  Agent social networks are just a hall of mirrors (subscribe to the newsletter for more amazing content) Mentioned in the episode: Matomo Tag Piper by David Vallejo Walker OS Jason Packer's new book: Google Analytics Alternatives Superweek Analytics Summit Measurecamp Helsinki Connect with Johan: Linkedin GA4BigQuery GA4Dataform This podcast is brought to you by Juliana Jackson and Simo Ahava.

In this episode, Michael Lynn (MongoDB) and Yang Li (Google Cloud) break down the architectural blueprint for building intelligent, production-grade applications. Move beyond simple RAG (Retrieval-Augmented Generation) and explore the world of AI Agents.What you'll learn:The Google Cloud AI stack: Vertex AI, Agent Space, and Model Garden.Deep-dive integration: Connecting MongoDB Atlas with BigQuery and Dataflow.Real-world Demo: Building a grocery store AI assistant using Gemini and Vector Search.Startup Perks: How to access up to $350k in Google Cloud credits and $10k in MongoDB credits.

Vincent Heuschling reçoit Hayssam Saleh, créateur de **Starlake**, une plateforme data open source française née de la factorisation de projets clients depuis 2017-2018. L'épisode intervient dans un contexte de consolidation du marché (rachat de DBT et de SQLMesh par Fivetran), qui invite à challenger les solutions établies.Starlake se distingue par une approche **entièrement déclarative** (YAML + SQL natif, sans Jinja) couvrant toute la chaîne data engineering : ingestion, transformation, orchestration et qualité des données. L'outil s'appuie sur les moteurs sous-jacents des plateformes cibles (Snowflake, BigQuery, Spark) et génère automatiquement les DAGs pour les orchestrateurs du marché (Airflow, Dagster, Snowflake Tasks).Parmi les fonctionnalités marquantes : le **data branching** (branches de données à la manière de Git), l'inférence automatique de schémas YAML à partir de fichiers sources, un **transpiler SQL** multi-plateformes, et l'extraction du lineage depuis du SQL brut sans annotation. L'intégration récente de **DuckLake** ouvre la voie à des architectures on-premise souveraines à coût maîtrisé (sous 300 €/mois sur OVH, Scaleway, Clever Cloud).Le modèle économique repose sur le support, la formation, et le consulting : Starlake s'installe dans le cloud du client, avec mise à jour automatique gérée par l'équipe, sans accès aux données.**Chapitres****00:00:27** – Introduction : consolidation du marché data (rachat de DBT et SQLMesh par Fivetran) et présentation de l'épisode**00:03:13** – Hayssam et la genèse de Starlake : parcours Spark/Scala, POC à 4 000 formats de fichiers (2017-2018)**00:09:51** – Architecture et philosophie : load, transform, orchestration unifiés en déclaratif (YAML + SQL natif, pas de Jinja)**00:00:18:18** – Starlake vs DBT : différences philosophiques, composabilité, fonctionnalités 100 % open source**00:00:22:20** – Data branching, Starlake Labs (pipe syntax, transpiler SQL, lineage) et expérience développeur (DuckDB local, UI point-and-click)**00:36:35** – Modèle open source et économique : licence Apache, support, formation, marketplace cloud souveraine**00:43:42** – DuckLake : alternative on-premise/cloud souverain (OVH, Scaleway, Clever Cloud) et comment contribuer / démarrer**Le BigdataHebdo**Le BigdataHebdo est le podcast Francophone de la Data et de l'IA.Retrouvez plus de 200 épisodes https://bigdatahebdo.comRejoignez la communauté sur le Slack https://join.slack.com/t/bigdatahebdo/shared_invite/zt-a931fdhj-8ICbl9dbsZZbTcze61rr~Q

[Expertpanelen] Avsnitt 154 med Johan Strand, senior digital analyst och partner på byrån Ctrl Digital, om de senaste nyheterna och trenderna inom digital analys. Allt från hur vi bör tänka kring att mäta AI-trafik och de tre typerna som blandas ihop. Till nya nyheter i Google Analytics som cross-channel budgeting och nya spännande rapporter, och hur BigQuerys Conversational Analytics och Data Agents låter dig chatta med din data utan SQL. Du får dessutom höra om: Varför AI-agenter ställer till det för mätning Att Microsoft Clarity börjar mäta bottrafik Headless commerce och när frontenden försvinner Varför cross-channel budgeting är så stort Den efterlängtade attributionsrapporten Hur Data Agents gör BigQuery mer tillgängligt Intersports GDPR-böter på 3,5 miljoner euro Om gästen Johan Strand är senior digital analyst och partner på Ctrl Digital, en av Sveriges ledande analytics-byråer. Han är otroligt vass på Google Analytics, BigQuery och att bygga datastrukturer som skapar affärsnytta. Som återkommande expert i poddens nyhetspanel delar Johan regelbundet sina analyser av de viktigaste förändringarna inom digital analys, spårning och datainsamling. Johan är också en av arrangörerna av MeasureCamp Malmö. Tidsstämplar [00:01:54] Mäta tre kategorier av AI-trafik. Reder ut skillnaden mellan “vanlig” AI-trafik, AI-webbläsare och AI-agenter, och hur vi bör tänka kring det. Samt varför Custom Channel Groups bara fångar en del. [00:21:19] Senaste Google Analytics-nyheterna. Nya funktioner som cross-channel budgeting, attributions- och kundreserapporter samt uppföljning kring cost/campaign data import och Analytics Advisor. [00:36:40] Conversational Analytics och Data Agents. Hur BigQuerys nya Data Agents låter dig skapa anpassade agenter för olika avdelningar och ställa frågor till din data i fritext utan SQL. [00:43:20] Lightning round med analysnyheter. Nya BigQuery-kopplingar för Shopify och Mailchimp, Intersports GDPR-böter på 3,5 miljoner euro i Frankrike och Digital Omnibus Act. Länkar Johan Strand på LinkedInCtrl Digital (webbsida) ChatGPT Atlas vs Perplexity Comet: Agentic Browsers – HUMAN Security (artikel) AI Browser Tracking: Marketer and Analyst Guide – Stape (artikel) Clarity AI Bot Activity – Microsoft (verktyg) Cross-channel budgeting plans – Google Analytics (dokumentation) Cross-channel conversion reporting – Google Analytics (dokumentation) Import campaign data – Google Analytics (dokumentation) Analytics Advisor – Google Analytics (dokumentation) Introducing Conversational Analytics in BigQuery – Google Cloud (artikel) Shopify Connector – Google Cloud (dokumentation) Mailchimp Connector – Google Cloud (dokumentation) Intersport Fined €3.5M for Customer Data Transfers – SGI Europe (artikel) Digitalenta (veckans sponsor)StickerApp (partnernätverket)Contentor (partnernätverket)Oderland (partnernätverket)

Kennst du diese Situation im Team: Jemand sagt "das skaliert nicht", und plötzlich steht der Datenbankwechsel schneller im Raum als die eigentliche Frage nach dem Warum? Genau da packen wir an. Denn in vielen Systemen entscheidet nicht das nächste hippe Tool von Hacker News, sondern etwas viel Grundsätzlicheres: Datenlayout und Zugriffsmuster.In dieser Episode gehen wir einmal tief runter in den Storage-Stack. Wir schauen uns an, warum Row-Oriented-Datastores der Standard für klassische OLTP-Workloads sind und warum "SELECT id" trotzdem oft fast genauso teuer ist wie "SELECT *". Danach drehen wir die Tabelle um 90 Grad: Column Stores für OLAP, Aggregationen über viele Zeilen, Spalten-Pruning, Kompression, SIMD und warum ClickHouse, BigQuery, Snowflake oder Redshift bei Analytics so absurd schnell werden können.Und dann wird es file-basiert: CSV bekommt sein verdientes Fett weg, Apache Parquet seinen Hype, inklusive Row Groups, Metadaten im Footer und warum das für Streaming und Object Storage so gut passt. Mit Apache Iceberg setzen wir noch eine Management-Schicht oben drauf: Snapshots, Time Travel, paralleles Schreiben und das ganze Data-Lake-Feeling. Zum Schluss landen wir da, wo es richtig weh tut, beziehungsweise richtig Geld spart: Storage und Compute trennen, Tiered Storage, Kafka Connect bis Prometheus und Observability-Kosten.Wenn du beim nächsten "das skaliert nicht" nicht direkt die Datenbank tauschen willst, sondern erst mal die richtigen Fragen stellen möchtest, ist das deine Folge.Bonus: DuckDB als kleines Taschenmesser für CSV, JSON und SQL kann dein nächstes Wochenend-Experiment werden.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

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 comme 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 utilise l'IA.On aborde :

The NYSE is developing a platform for 24/7 onchain equities. Coinbase and Circle onboard the Bermuda government onchain. MegaETH announces a global network stress test. And ENS launches a public dataset on BigQuery. Read more: https://ethdaily.io/864 Sponsor: Arkiv is an Ethereum-aligned data layer for Web3. Arkiv brings the familiar concept of a traditional Web2 database into the Web3 ecosystem. Find out more at Arkiv.network Disclaimer: Content is for informational purposes only, not endorsement or investment advice. The accuracy of information is not guaranteed.

Google is rolling out managed MCP servers to make its services “agent-ready by design,” starting with Maps and BigQuery, aiming to simplify messy integrations and help AI agents use real tools. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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.

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.

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.

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.

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

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.

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.

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

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"

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!

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!