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In this talk, Xia He-Bleinagel, Head of Data & Cloud at NOW GmbH, shares her remarkable journey from studying automotive engineering across Europe to leading modern data, cloud, and engineering teams in Germany. We dive into her transition from hands-on engineering to leadership, how she balanced family with career growth, and what it really takes to succeed in today’s cloud, data, and AI job market.

TIMECODES: 00:00 Studying Automotive Engineering Across Europe 08:15 How Andrew Ng Sparked a Machine Learning Journey 11:45 Import–Export Work as an Unexpected Career Boos t17:05 Balancing Family Life with Data Engineering Studies 20:50 From Data Engineer to Head of Data & Cloud 27:46 Building Data Teams & Tackling Tech Debt 30:56 Learning Leadership Through Coaching & Observation 34:17 Management vs. IC: Finding Your Best Fit 38:52 Boosting Developer Productivity with AI Tools 42:47 Succeeding in Germany’s Competitive Data Job Market 46:03 Fast-Track Your Cloud & Data Career 50:03 Mentorship & Supporting Working Moms in Tech 53:03 Cultural & Economic Factors Shaping Women’s Careers 57:13 Top Networking Groups for Women in Data 1:00:13 Turning Domain Expertise into a Data Career Advantage

Connect with Xia- Linkedin - https://www.linkedin.com/in/xia-he-bleinagel-51773585/ - Github - https://github.com/Data-Think-2021 - Website - https://datathinker.de/

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/

NODES, the biggest community gathering dedicated to graph-powered apps, knowledge graphs, and AI, returns on November 6, 2025, for its seventh year.

Join thousands of developers and data and AI practitioners for this free, 24-hour conference to learn about the latest advancements in intelligent systems and gain insights from speakers who showcase their implementations, tools, and models.

You can expect: 📣 140+ Unique Live Technical Sessions 🥳 Fireside Chat with Andrew Ng and Emil Eifrém 🎉 AI Strategy Keynote from Daimler ⏰ 24 Hours in All Time Zones 📚 4 Tracks: AI Engineering, App Dev, Data Intelligence, and Knowledge Graphs

Don't miss it. Register now. It's free!

NODES, the biggest community gathering dedicated to graph-powered apps, knowledge graphs, and AI, returns on November 6, 2025, for its seventh year.

Join thousands of developers and data and AI practitioners for this free, 24-hour conference to learn about the latest advancements in intelligent systems and gain insights from speakers who showcase their implementations, tools, and models.

You can expect: 📣 140+ Unique Live Technical Sessions 🥳 Fireside Chat with Andrew Ng and Emil Eifrém 🎉 AI Strategy Keynote from Daimler ⏰ 24 Hours in All Time Zones 📚 4 Tracks: AI Engineering, App Dev, Data Intelligence, and Knowledge Graphs

Don't miss it. Register now. It's free!

NODES, the biggest community gathering dedicated to graph-powered apps, knowledge graphs, and AI, returns on November 6, 2025, for its seventh year.

Join thousands of developers and data and AI practitioners for this free, 24-hour conference to learn about the latest advancements in intelligent systems and gain insights from speakers who showcase their implementations, tools, and models.

You can expect: 📣 140+ Unique Live Technical Sessions 🥳 Fireside Chat with Andrew Ng and Emil Eifrém 🎉 AI Strategy Keynote from Daimler ⏰ 24 Hours in All Time Zones 📚 4 Tracks: AI Engineering, App Dev, Data Intelligence, and Knowledge Graphs

Don't miss it. Register now. It's free!

We are welcoming you to join 31st edition of PyData Trójmiasto meetup!

• When: 18th of June, at 18:00

• Where: Room 002, Building B, Gdańsk Science-Technology Park.

• Registration: event is free to join, just let us know you're coming by RSVP on meetup to estimate pizzas.

Agenda: 18:00 - 18:05 - Meeting boarding 18:05 - 18:10 - A few words about PyData 18:10 - 18:50 - Divide and Conquer in the LLM world - LLM agents by Sebastian Chwilczyński 18:50 - 19:35 - Bridging LLM and Databases: Lessons learned in production by Mateusz Hordyński 19:35 - Pizza & Networking

About Divide and Conquer in the LLM world Andrew Ng said that AI agent workflows will drive massive AI progress this year, and all AI practitioners should pay attention to this trend. I'm the last one to undermine his words, so let's dive deep into agentic systems. In this presentation, we are going to explore the four most fundamental design patterns for Agent Systems: Reflection, Tools Use, Planning, and Collaboration. Along the way, we'll learn some best practices and examine recent use cases to see these principles in action.

About Sebastian Sebastian Chwilczynski is a Machine Learning Engineer at deepsense.ai. He graduated in Artificial Intelligence from Poznań University of Technology. Recently, in his professional work, he's been mostly focused on making LLM systems more robust. Good research before the implementation is even more important than a morning coffee.

About Bridging LLM and Databases: Lessons learned in production Prepare to dive into the exciting world of Large Language Models (LLMs) and structured data sources. In this session, we'll shed light on how to link LLMs to relational databases. We'll explore case studies and share different tried-and-tested approaches to achieve this. You'll understand the benefits, the obstacles, and what's on the horizon for integrating LLMs and structured data. Whether you're a seasoned professional in data science or just starting out, this talk has something to help you improve your skills in working with LLMs and external structured data sources.

About Mateusz I'm a software engineer who specializes in creating big data architectures for both cloud and on-premises setups. Currently, I'm a Technical Leader at deepsense.ai, where I design generative AI applications and build data pipelines to support them. I'm also the lead maintainer of the open-source project db-ally. Outside of work, I try to live the digital nomad lifestyle, which has taught me how to work remotely from some pretty weird office setups.

We are deeply thankful to Gdańsk Science-Technology Park for hosting us. Vemco Sp. z o.o. for sponsoring tasty pizzas! And to JetBrains for the possibility to give away a few licenses for their tools - in exchange of interesting questions!;)

See you at PyData!

L'avenir est Agentique ! Comme l'ont souligné Andrew Ng et Andrej Karpathy, le futur de l'intelligence artificielle est agentique. Mais qu'entend-on exactement par "systèmes agentiques" et "agents d'intelligence artificielle" (agents IA) ?

Définition des Agents d'IA Les agents d'IA sont des systèmes ou programmes autonomes capables d'effectuer des tâches, de prendre des décisions et d'interagir avec les utilisateurs ou d'autres systèmes d'une manière similaire aux humains. Contrairement aux systèmes d'IA traditionnels qui génèrent un résultat unique basé sur une invite donnée, les agents d'IA collaborent, partagent des objectifs et prennent des décisions collectives pour accomplir les tâches plus efficacement.

Avantages des Agents d'IA Cette approche collaborative permet des interactions et des processus décisionnels plus sophistiqués, conduisant finalement à une efficacité améliorée et à des résultats nettement plus précis dans diverses applications. Les agents d'IA disposent d'un réel pouvoir d'action et peuvent s'adapter dynamiquement à leur environnement pour atteindre leurs objectifs de manière optimale.

Démonstration en Direct Lors de cette session, je vous présenterai en détail les systèmes agentiques et les agents d'IA, suivie d'une démonstration en direct pour vous permettre de mieux comprendre leur fonctionnement et leur potentiel.

Rejoignez-nous nombreux pour cette présentation passionnante sur l'avenir de l'intelligence artificielle !

Registration Time - 5:30 PM Networking - 30 mins Start time with Introduction - 6 pm Speaker I - Andrew Sweet (dbt Labs)- Deeper Dive into Column Level Lineage (CLL) and its Application in Data Modeling - 25 mins Q&A - 5 mins Speaker II - Olya Solomon\, Yvonne Teng\, Anna Ng (Slalom\, Project Delivery Team) - Real life dbt application in modern Data & Analytics platform- 25 mins Q&A - 5 mins Social, Food, Drink

Refreshments will be provided by Slalom (pizza, drinks, etc.)

Our venue has capacity limits, so please only RSVP if you intend to come and and reach out to [email protected] if you need to cancel last minute or change your RSVP status on the Meetup to "Not Going." DIRECTIONS: Slalom is located in 2 Logan Square, 100 N. 18th Street, Philadelphia. Short walk from Septa Suburban Station. Closest parking garage address is 1815 Cherry St. Give your name at Security, have picture ID and they will let you up to the 20th floor.

What are dbt Meetups? dbt Meetups are networking events open to all folks working with data! Talks predominantly focus on community members' experience with dbt, however, you'll catch presentations on broader topics such as analytics engineering, data stacks, data ops, modeling, testing, and team structures.

To attend, please read the Health and Safety Policy and Terms of Participation: https://bit.ly/4azcreT

➡️ Join the dbt Slack community: https://www.getdbt.com/community/ 🤝For the best Meetup experience, make sure to join the #local-phl channel in dbt Slack (https://slack.getdbt.com/).

It's been almost a year since ChatGPT was released, mainstreaming AI into the collective consciousness in the process. Since that moment, we've seen a really spirited debate emerge within the data & AI communities, and really public discourse at large. The focal point of this debate is whether AI is or will lead to existential risk for the human species at large. We've seen thinkers such as Elizier Yudkowski, Yuval Noah Harari, and others sound the alarm bell on how AI is as dangerous, if not more dangerous than nuclear weapons. We've also seen AI researchers and business leaders sign petitions and lobby government for strict regulation on AI.  On the flip side, we've also seen luminaries within the field such as Andrew Ng and Yan Lecun, calling for, and not against, the proliferation of open-source AI. So how do we maneuver this debate, and where does the risk spectrum actually lie with AI? More importantly, how can we contextualize the risk of AI with other systemic risks humankind faces? Such as climate change, risk of nuclear war, and so on and so forth? How can we regulate AI without falling into the trap of regulatory capture—where a select and mighty few benefit from regulation, drowning out the competition in the meantime? Trond Arne Undheim is a Research scholar in Global Systemic Risk, Innovation, and Policy at Stanford University, Venture Partner at Antler, and CEO and co-founder of Yegii, an insight network with experts and knowledge assets on disruption. He is a nonresident Fellow at the Atlantic Council with a portfolio in artificial intelligence, future of work, data ethics, emerging technologies, and entrepreneurship. He is a former director of MIT Startup Exchange and has helped launch over 50 startups. In a previous life, he was an MIT Sloan School of Management Senior Lecturer, WPP Oracle Executive, and EU National Expert. In this episode, Trond and Adel explore the multifaceted risks associated with AI, the cascading risks lens and the debate over the likelihood of runaway AI. Trond shares the role of governments and organizations in shaping AI's future, the need for both global and regional regulatory frameworks, as well as the importance of educating decision-makers on AI's complexities. Trond also shares his opinion on the contrasting philosophies behind open and closed-source AI technologies, the risk of regulatory capture, and more.  Links mentioned in the show: Augmented Lean: A Human-Centric Framework for Managing Frontline Operations by Trond Arne Undheim & Natan LinderFuture Tech: How to Capture Value from Disruptive Industry Trends Trond Arne UndheimFuturized PodcastStanford Cascading Risk StudyCourse: AI Ethics

No episódio de hoje, Paulo Vasconcellos, Allan Sene e Gabriel Lages falam sobre um polêmico tema na indústria de dados: como o termo Big Data vai morrer. Essa conversa surgiu após uma entrevista com Andrew Ng, um dos pioneiros em AI no mundo, falando sobre darmos lugar a uma coleta de dados mais inteligente e ligada a um contexto.

Baixe o dataset do State of Data Brazil no Kaggle (https://www.kaggle.com/datasets/datahackers/state-of-data-2021)

Acesse o post no Medium para ter acesso as referências do episódio (https://www.kaggle.com/datasets/datahackers/state-of-data-2021)

Summary Spark is one of the most well-known frameworks for data processing, whether for batch or streaming, ETL or ML, and at any scale. Because of its popularity it has been deployed on every kind of platform you can think of. In this episode Jean-Yves Stephan shares the work that he is doing at Data Mechanics to make it sing on Kubernetes. He explains how operating in a cloud-native context simplifies some aspects of running the system while complicating others, how it simplifies the development and experimentation cycle, and how you can get a head start using their pre-built Spark container. This is a great conversation for understanding how new ways of operating systems can have broader impacts on how they are being used.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Jean-Yves Stephan about Data Mechanics, a cloud-native Spark platform for data engineers

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what you are building at Data Mechanics and the story behind it? What are the operational characteristics of Spark that make it difficult to run in a cloud-optimized environment? How do you handle retries, state redistribution, etc. when instances get pre-empted during the middle of a job execution?

What are some of the tactics that you have found useful when designing jobs to make them more resilient to interruptions?

What are the customizations that you have had to make to Spark itself? What are some of the supporting tools that you have built to allow for running Spark in a Kubernetes environment? How is the Data Mechanics platform implemented?

How have the goals and design of the platform changed or evolved since you first began working on it?

How does running Spark in a container/Kubernetes environment change the ways that you and your customers think about how and where to use it?

How does it impact the development workflow for data engineers and data scientists?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while building the Data Mechanics product? When is Spark/Data Mechanics the wrong choice? What do you have planned for the future of the platform?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Data Mechanics Databricks Stanford Andrew Ng Mining Massive Datasets Spark Kubernetes Spot Instances Infiniband Data Mechanics Spark Container Image Delight – Spark monitoring utility Terraform Blue/Green Deployment Spark Operator for Kubernetes JupyterHub Jupyter Enterprise Gateway

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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