Learn how to accelerate and automate migrations with SnowConvert AI, featuring data ecosystem migration agents powered by Snowflake Cortex AI. SnowConvert AI is your free, automated solution designed to dramatically reduce the complexities, costs, and timelines associated with data warehouse and BI migrations. It intelligently analyzes your existing code, automating code conversion, data validation, and streamlining the entire migration process.

Master AI-powered analytics in Snowflake. Learn how to leverage Snowflake's powerful AI SQL functions for advanced analytics on unstructured, time-series, and geospatial data. We'll also demonstrate how to use Semantic Views to bridge the gap between data and business understanding.

Evoke, the leading betting and gaming company behind brands like William Hill, 888, Mr Green and Winner, has unified its data with Tealium and Snowflake to power product analytics, AI, and real-time insights. Discover how this transformation drives speed, compliance, and personalized customer experiences—and what’s next on their data journey.

Executives don’t just ask what happened- they want to know why. Answering those questions quickly and consistently is where AI often falls short.

This workshop will show you how to teach AI to answer “why” by grounding it in a semantic layer. You’ll learn:

•What a semantic layer is and how it enables consistent answers •How to model relationships and metrics for clarity and trust •How to leverage semantic views on Snowflake •How to enable AI using Cortex to provide reliable, self-serve analytics •How to extend AI to generate deeper analyses you can depend on Prepare for the session by signing up through this link: https://bit.ly/honeydew-swt-2025

By the end, you’ll be equipped to give AI the foundation it needs to explain why - delivering faster, more consistent insights your business can rely on.

This presentation outlines the transformation from legacy systems to a modern data and AI platform within Toyota Material Handling Europe. It highlights the strategic adoption of Snowflake to unify data architecture, enable real-time analytics, and support external data sharing. The journey includes the foundation and evolution of an internal AI initiative, DataLabs, which matured into a full-scale AI program.

The retail and consumer goods industries are undergoing significant transformation, driven by shifting consumer behaviors, global economic changes, supply chain disruptions and, most importantly, rapid technological innovation. This session is designed for business and technology leaders, offering them insights and strategies needed to navigate and thrive in this evolving landscape. Learn from the transformational experience of the leading global consumer goods company, Snowflake industry expert exploring how data and AI technologies are shaping the industries' future.

In this episode, I’m exploring the mindset shift data professionals need to make when moving into analytics and AI data product management. From how to ask the right questions to designing for meaningful adoption, I share four key ways to think more like a product manager, and less like a deliverables machine, so your data products earn applause instead of a shoulder shrug.

Highlights/ Skip to:

Why shift to analytics and AI data product management (00:34) From accuracy to impact and redefining success with AI and analytical data products  (01:59) Key Idea 1: Moving from question asker (analyst) to problem seeker (product) (04:31) Key Idea 2: Designing change management into solutions; planning for adoption starts in the design phase (12:52) Key Idea 3: Creating tools so useful people can’t imagine working without them. (26:23) Key Idea 4: Solving for unarticulated needs vs. active needs (34:24)

Quotes from Today’s Episode “Too many analytics teams are rewarded for accuracy instead of impact. Analysts give answers, and product people ask questions.The shift from analytics to product thinking isn’t about tools or frameworks, it’s about curiosity.It’s moving from ‘here’s what the data says’ to ‘what problem are we actually trying to solve, and for whom?’That’s where the real leverage is, in asking better questions, not just delivering faster answers.”

“We often mistake usage for success.Adoption only matters if it’s meaningful adoption. A dashboard getting opened a hundred times doesn’t mean it’s valuable... it might just mean people can’t find what they need.Real success is when your users say, ‘I can’t imagine doing my job without this.’That’s the level of usefulness we should be designing for.”

“The most valuable insights aren’t always the ones people ask for. Solving active problems is good, it’s necessary. But the big unlock happens when you start surfacing and solving latent problems, the ones people don’t think to ask for.Those are the moments when users say, ‘Oh wow, that changes everything.’That’s how data teams evolve from service providers to strategic partners.”

“Here’s a simple but powerful shift for data teams: know who your real customer is. Most data teams think their customer is the stakeholder who requested the work… But the real customer is the end user whose life or decision should get better because of it. When you start designing for that person, not just the requester, everything changes: your priorities, your design, even what you choose to measure.”

Links

Need 1:1 help to navigate these questions and align your data product work to your career? Explore my new Cross-Company Group Coaching at designingforanalytics.com/groupcoaching

For peer support: the Data Product Leadership Community where peers are experimenting with these approaches. designingforanalytics.com/community

DNB, Norway’s largest bank, began building a cloud-based self-service Data & AI Platform in 2017, delivering its first capabilities by 2018. Initially focused on ML and analytics, the platform expanded in 2021 to include traditional data warehouses and modern data products. Snowflake was officially launched in 2023 after a successful PoC and pilot.

In this talk, we’ll walk through our journey.

Where We Came From

•Discover how legacy data warehouse bottlenecks sparked a shift toward decentralised, self-service data capabilities.

Where We Are

•Learn how DNB enabled teams to own and operate their data products through: •Streamlined domain onboarding •“DevOps for data” and “SQL as code” practices •Automated services for historisation (PSA)

Where We’re Going

•Explore how DNB is evolving its data mesh with: •A hybrid model of decentralised and centralised data products •Generative AI, metadata automation, and development support •Enhanced tooling and services for data consumers

Explore Snowflake’s enterprise AI vision, innovations, and roadmap. You’ll see how Snowflake Intelligence, Cortex AI SQL, Cortex Agents, automatic Semantic Model generation and specialized AI tools work together inside Snowflake’s secure platform, turning multimodal data into transformational impact for enterprise customers without infrastructure headaches. Walk away knowing how these integrated capabilities give business users, data analysts and engineers the tools, speed and governance required to deploy AI into production.

Learn how to leverage Snowflake Intelligence to securely interact with your organization's structured and unstructured data using natural language, focusing on Cortex Knowledge Extensions (CKEs). You will learn to:

•Set up your Snowflake environment, including creating a database, schema, and role. •Integrate Cortex Knowledge Extensions from the Snowflake Marketplace. •Create and configure an AI agent within Snowflake Intelligence to interact with your data. •Add instructions and sample questions to your agent to enable natural language interaction. By the end of this session, you will have a working understanding of how to use Snowflake Intelligence and Cortex Knowledge Extensions to build Enterprise ready Data Agent applications in Snowflake.This is a Hands-On workshop, all attendees must bring their own laptop to participate.

Discover how Scania’s Smart Factory Lab uses Snowflake to scale AI for laser welding quality control. In this session, it is shown how ultrasonic scan data is transformed into AI-driven insights that detect weld flaws faster and more accurately. Learn how Snowpark Container Services and ML pipelines enable seamless model training, deployment, and monitoring to boost fast development and scalability.

Join Maria Wiss, Daniel Myers, and Angela Carpenter, as they kickoff Snowflake World Tour Stockholm, share vision for the AI Data Cloud, customer stories and make exciting product announcements.

Partner Summit is for dbt Labs Partners only Join us for an exclusive partner-focused event where you’ll gain insights into dbt’s strategic vision, discover new growth opportunities, and connect directly with the team driving ecosystem innovation. Be the first to hear the latest product and company updates, explore the role of dbt in AI-driven initiatives, and learn how our evolving partner programs will help you build your business with dbt.

The role of data analysts is evolving, not disappearing. With generative AI transforming the industry, many wonder if their analytical skills will soon become obsolete. But how is the relationship between human expertise and AI tools really changing? While AI excels at coding, debugging, and automating repetitive tasks, it struggles with understanding complex business problems and domain-specific challenges. What skills should today's data professionals focus on to remain relevant? How can you leverage AI as a partner rather than viewing it as a replacement? The balance between technical expertise and business acumen has never been more critical in navigating this changing landscape. Mo Chen is a Data & Analytics Manager with over seven years of experience in financial and banking data. Currently at NatWest Group, Mo leads initiatives that enhance data management, automate reporting, and improve decision-making across the organization. After earning an MSc in Finance & Economics from the University of St Andrews, Mo launched a career in risk and credit portfolio management before transitioning into analytics. Blending economics, finance, and data engineering, Mo is skilled at turning large-scale financial data into actionable insight that supports efficiency and strategic planning. Beyond corporate life, Mo has become a passionate educator and community-builder. On YouTube, Mo hosts a fast-growing channel (185K+ subscribers, with millions of views) where he breaks down complex analytics concepts into bite-sized, actionable lessons. In the episode, Richie and Mo explore the evolving role of data analysts, the impact of AI on coding and debugging, the importance of domain knowledge for career switchers, effective communication strategies in data analysis, and much more. Links Mentioned in the Show: Mo’s Website - Build a Data Portfolio WebsiteMo’s YouTube ChannelConnect with MoGet Certified as a Data AnalystRelated Episode: Career Skills for Data Professionals with Wes Kao, Co-Founder of MavenRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Summary In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Nick Schrock, CTO and founder of Dagster Labs, to discuss Compass - a Slack-native, agentic analytics system designed to keep data teams connected with business stakeholders. Nick shares his journey from initial skepticism to embracing agentic AI as model and application advancements made it practical for governed workflows, and explores how Compass redefines the relationship between data teams and stakeholders by shifting analysts into steward roles, capturing and governing context, and integrating with Slack where collaboration already happens. The conversation covers organizational observability through Compass's conversational system of record, cost control strategies, and the implications of agentic collaboration on Conway's Law, as well as what's next for Compass and Nick's optimistic views on AI-accelerated software engineering.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Nick Schrock about building an AI analyst that keeps data teams in the loopInterview IntroductionHow did you get involved in the area of data management?Can you describe what Compass is and the story behind it?context repository structurehow to keep it relevant/avoid sprawl/duplicationproviding guardrailshow does a tool like Compass help provide feedback/insights back to the data teams?preparing the data warehouse for effective introspection by the AILLM selectioncost managementcaching/materializing ad-hoc queriesWhy Slack and enterprise chat are important to b2b softwareHow AI is changing stakeholder relationshipsHow not to overpromise AI capabilities How does Compass relate to BI?How does Compass relate to Dagster and Data Infrastructure?What are the most interesting, innovative, or unexpected ways that you have seen Compass used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Compass?When is Compass the wrong choice?What do you have planned for the future of Compass?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links DagsterDagster LabsDagster PlusDagster CompassChris Bergh DataOps EpisodeRise of Medium Code blog postContext EngineeringData StewardInformation ArchitectureConway's LawTemporal durable execution frameworkThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

A case is made that the US floor is low but the ceiling is high, while the range for the rest of the world is narrower. A counter argument is that the bullish view of risk assets poses more of an asymmetric risk distribution for the US. Fiscal policies have tilted easier this year, a theme we stressed in our year-ahead outlook, but the latest developments (France, Japan, China, US) do not move the global needle much. 

Speakers:

Bruce Kasman

Joseph Lupton

This podcast was recorded on 10 October 2025.

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

In this episode, we talked with Aishwarya Jadhav, a machine learning engineer whose career has spanned Morgan Stanley, Tesla, and now Waymo. Aishwarya shares her journey from big data in finance to applied AI in self-driving, gesture understanding, and computer vision. She discusses building an AI guide dog for the visually impaired, contributing to malaria mapping in Africa, and the challenges of deploying safe autonomous systems. We also explore the intersection of computer vision, NLP, and LLMs, and what it takes to break into the self-driving AI industry.TIMECODES00:51 Aishwarya’s career journey from finance to self-driving AI05:45 Building AI guide dog for the visually impaired12:03 Exploring LiDAR, radar, and Tesla’s camera-based approach16:24 Trust, regulation, and challenges in self-driving adoption19:39 Waymo, ride-hailing, and gesture recognition for traffic control24:18 Malaria mapping in Africa and AI for social good29:40 Deployment, safety, and testing in self-driving systems37:00 Transition from NLP to computer vision and deep learning43:37 Reinforcement learning, robotics, and self-driving constraints51:28 Testing processes, evaluations, and staged rollouts for autonomous driving52:53 Can multimodal LLMs be applied to self-driving?55:33 How to get started in self-driving AI careersConnect with Aishwarya- Linkedin - https://www.linkedin.com/in/aishwaryajadhav8/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

In this episode, we talked with Ranjitha Kulkarni, a machine learning engineer with a rich career spanning Microsoft, Dropbox, and now NeuBird AI. Ranjitha shares her journey into ML and NLP, her work building recommendation systems, early AI agents, and cutting-edge LLM-powered products. She offers insights into designing reliable AI systems in the new era of generative AI and agents, and how context engineering and dynamic planning shape the future of AI products.TIMECODES00:00 Career journey and early curiosity04:25 Speech recognition at Microsoft05:52 Recommendation systems and early agents at Dropbox07:44 Joining NewBird AI12:01 Defining agents and LLM orchestration16:11 Agent planning strategies18:23 Agent implementation approaches22:50 Context engineering essentials30:27 RAG evolution in agent systems37:39 RAG vs agent use cases40:30 Dynamic planning in AI assistants43:00 AI productivity tools at Dropbox46:00 Evaluating AI agents53:20 Reliable tool usage challenges58:17 Future of agents in engineering Connect with Ranjitha- Linkedin - https://www.linkedin.com/in/ranjitha-gurunath-kulkarniConnect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

In this episode, we talked with Abouzar Abbaspour, a data engineer whose career spans software engineering in Iran, building crowd and recommendation systems at a Dutch theme park, deploying large-scale ML models at Bol.com, and now working at Tesla. Abouzar shares how he bridged diverse industries, tackled real-world data challenges, and adapted to new roles while keeping a hands-on approach to machine learning and engineering.TIMECODES00:00 Career journey and early motivations06:17 Moving to Europe for data science12:18 Working with theme parks and crowd modeling18:29 Lessons from ride and visitor data23:06 Building recommendation systems at Efteling27:26 Joining Bol.com and the Dutch e-commerce industry32:49 Product and brand recommendation logic36:09 Experimenting with "Tinder for brands"40:26 Engagement metrics and product validation43:02 From ML engineering to data engineering roles52:04 Hands-on skills at Tesla and industry expectations57:43 Career growth, learning, and adviceConnect with AbouzarLinkedin -   / abouzar-abbaspour   Website - https://www.abouzar-abbaspour.com/ Connect with DataTalks.Club: Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...Check other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn -   / datatalks-club   Twitter -   / datatalksclub   Website - https://datatalks.club/

In this episode, we chat with Dashel Ruiz, whose journey spans semiconductors, machine learning, and teaching. Dashel shares how he transitioned from hardware to data science, navigated complex projects in diverse industries, and now combines technical expertise with a passion for teaching. Tune in to hear insights on building a career in data, mastering new technologies, and making an impact both in the lab and the classroom.

TIMECODES 00:00 Dashel's unique career path from music to semiconductors 06:16 The transition into data and software engineering at Microchip 11:44 Discovering machine learning to solve real problems in semiconductor manufacturing 20:40 How Dashel found and his experience with the Machine Learning Zoomcamp 29:33 The practical advantages of DataTalks.Club courses over other platforms 39:52 Overcoming challenges and the value of the learning community 48:10 Hands-on project experience: From image classification to Kaggle competitions 54:12 Staying motivated throughout the long-term course 59:55 The importance of deployment and full-stack ML skills 1:07:36 Closing thoughts on teaching and future courses

Connect with Dashel Linkedin - https://www.linkedin.com/in/dashel-ruiz-perez-2b036172/ Connect with DataTalks.Club: Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/