La souveraineté des données et des intelligences artificielles est un enjeu stratégique majeur pour les entreprises et les institutions. Dans cette keynote, nous explorerons comment développer des services innovants basés sur le cloud et l'IA tout en gardant le contrôle total de vos systèmes d'information et de vos applications data. Nous mettrons en lumière les défis et les opportunités associés à la gestion sécurisée des données, à la protection de la vie privée et à l'autonomie technologique des organisations. En abordant des thèmes comme le cloud souverain, les modèles d'IA responsables, la réversibilité des solutions et l'open-source, cette présentation offrira un aperçu essentiel des solutions permettant aux acteurs du numérique de maîtriser leur avenir tout en respectant les valeurs de liberté de choix et de sécurité.

Explore how Siemens is transforming data sharing with innovative data products, powered by Snowflake for seamless, automated, and cross-platform data sharing. This transformative approach empowers Siemens to enhance collaboration, and unlock the full potential of enterprise data, paving the way to becoming a truly data-driven organization. Join us to explore their journey and key insights.

Where data does more—analytics, AI, data engineering, apps, and collaboration. Powered with the AI Data Cloud.

Discussion on chips and compute for DACH, including EU landscape (Cerebras/SiPearl), GPUs, cloud vs on-prem, TCO and energy considerations.

Keynote talk by Alenka Frim, Apache Arrow maintainer, data engineer at United.Cloud.

Ace the DP-300 Exam with this essential study companion, chock-full of insights and tips you cannot find online. This book will help you build a comprehensive understanding of Azure SQL systems and their role in supporting business solutions, and it will equip you with the mental models and technical knowledge needed to confidently answer exam questions. Structured to align with Microsoft’s published study guide, the book spans five major sections that correspond to the skills measured by the exam, covering topics vital to modern cloud operations and including HA/DR, security, compliance, performance, and scalability. [if !supportAnnotations]You’ll also learn about the ways cloud operations have changed the focus of operating database systems from task execution to platform configuration—and how to configure your data platforms to meet this new reality. [if !supportAnnotations] By the end of this book, you’ll be prepared to navigate exam scenarios with finesse, pass the exam with confidence, and advance in your career with a solid foundation of knowledge. What You Will Learn Maximize your ability to benefit from the online learning tools for Exam DP-300 Gain depth and context for Azure SQL technical solutions relevant to Exam DP-300 Boost your confidence in Azure SQL Database skills Extend your on-premises SQL Server skill set into the Azure SQL cloud Enhance your overall understanding of Azure SQL administration and operations Develop your Azure SQL skill set to increase your value as an employee or contractor Adopt a new mindset for cloud-based solutions versus on-premises solutions Who This Book Is For Anyone planning to take the DP-300: Administering Microsoft Azure SQL Solutions exam, and those who wish to understand Azure SQL and how to successfully migrate and manage SQL solutions using all Azure SQL Technologies

Master the art of data transformation with the second edition of this trusted guide to dbt. Building on the foundation of the first edition, this updated volume offers a deeper, more comprehensive exploration of dbt’s capabilities—whether you're new to the tool or looking to sharpen your skills. It dives into the latest features and techniques, equipping you with the tools to create scalable, maintainable, and production-ready data transformation pipelines. Unlocking dbt, Second Edition introduces key advancements, including the semantic layer, which allows you to define and manage metrics at scale, and dbt Mesh, empowering organizations to orchestrate decentralized data workflows with confidence. You’ll also explore more advanced testing capabilities, expanded CI/CD and deployment strategies, and enhancements in documentation—such as the newly introduced dbt Catalog. As in the first edition, you’ll learn how to harness dbt’s power to transform raw data into actionable insights, while incorporating software engineering best practices like code reusability, version control, and automated testing. From configuring projects with the dbt Platform or open source dbt to mastering advanced transformations using SQL and Jinja, this book provides everything you need to tackle real-world challenges effectively. What You Will Learn Understand dbt and its role in the modern data stack Set up projects using both the cloud-hosted dbt Platform and open source project Connect dbt projects to cloud data warehouses Build scalable models in SQL and Python Configure development, testing, and production environments Capture reusable logic with Jinja macros Incorporate version control with your data transformation code Seamlessly connect your projects using dbt Mesh Build and manage a semantic layer using dbt Deploy dbt using CI/CD best practices Who This Book Is For Current and aspiring data professionals, including architects, developers, analysts, engineers, data scientists, and consultants who are beginning the journey of using dbt as part of their data pipeline’s transformation layer. Readers should have a foundational knowledge of writing basic SQL statements, development best practices, and working with data in an analytical context such as a data warehouse.

Pour celles et ceux qui n'ont peu ou pas d'experience avec le cloud, ça peut paraître un peu effrayant de se lancer. Cette session va vous montrer les premières étapes à suivre vers son premier succès. A partir de là, on verra comment adopter au fur et à mesure des composants natifs du Cloud pour déveloper plus vite, adopter des bonnes pratiques, et optimiser les coûts

Summary In this crossover episode of the AI Engineering Podcast, host Tobias Macey interviews Brijesh Tripathi, CEO of Flex AI, about revolutionizing AI engineering by removing DevOps burdens through "workload as a service". Brijesh shares his expertise from leading AI/HPC architecture at Intel and deploying supercomputers like Aurora, highlighting how access friction and idle infrastructure slow progress. Join them as they discuss Flex AI's innovative approach to simplifying heterogeneous compute, standardizing on consistent Kubernetes layers, and abstracting inference across various accelerators, allowing teams to iterate faster without wrestling with drivers, libraries, or cloud-by-cloud differences. Brijesh also shares insights into Flex AI's strategies for lifting utilization, protecting real-time workloads, and spanning the full lifecycle from fine-tuning to autoscaled inference, all while keeping complexity at bay.

Pre-amble I hope you enjoy this cross-over episode of the AI Engineering Podcast, another show that I run to act as your guide to the fast-moving world of building scalable and maintainable AI systems. As generative AI models have grown more powerful and are being applied to a broader range of use cases, the lines between data and AI engineering are becoming increasingly blurry. The responsibilities of data teams are being extended into the realm of context engineering, as well as designing and supporting new infrastructure elements that serve the needs of agentic applications. This episode is an example of the types of work that are not easily categorized into one or the other camp.

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 Brijesh Tripathi about FlexAI, a platform offering a service-oriented abstraction for AI workloadsInterview IntroductionHow did you get involved in machine learning?Can you describe what FlexAI is and the story behind it?What are some examples of the ways that infrastructure challenges contribute to friction in developing and operating AI applications?How do those challenges contribute to issues when scaling new applications/businesses that are founded on AI?There are numerous managed services and deployable operational elements for operationalizing AI systems. What are some of the main pitfalls that teams need to be aware of when determining how much of that infrastructure to own themselves?Orchestration is a key element of managing the data and model lifecycles of these applications. How does your approach of "workload as a service" help to mitigate some of the complexities in the overall maintenance of that workload?Can you describe the design and architecture of the FlexAI platform?How has the implementation evolved from when you first started working on it?For someone who is going to build on top of FlexAI, what are the primary interfaces and concepts that they need to be aware of?Can you describe the workflow of going from problem to deployment for an AI workload using FlexAI?One of the perennial challenges of making a well-integrated platform is that there are inevitably pre-existing workloads that don't map cleanly onto the assumptions of the vendor. What are the affordances and escape hatches that you have built in to allow partial/incremental adoption of your service?What are the elements of AI workloads and applications that you are explicitly not trying to solve for?What are the most interesting, innovative, or unexpected ways that you have seen FlexAI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on FlexAI?When is FlexAI the wrong choice?What do you have planned for the future of FlexAI?Contact Info LinkedInParting Question From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Links Flex AIAurora Super ComputerCoreWeaveKubernetesCUDAROCmTensor Processing Unit (TPU)PyTorchTritonTrainiumASIC == Application Specific Integrated CircuitSOC == System On a ChipLoveableFlexAI BlueprintsTenstorrentThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

A data scientist by day and a standup comedian by night. This was how Arda described himself prior to his critically acclaimed performance about his two identities during PyData 2024, where they merged.

Now he doesn't even know.

After another year of stage performances, awkward LinkedIn interactions and mysterious cloud errors, Arda is back for another tale of absurdity. In this closing talk, he will illustrate the hilarity of his life as a data scientist in the age of LLMs and his non-existent comfort zone, proving good sequels can exist

Metaflow is a powerful workflow management framework for data science, but optimizing its cloud resource usage still involves guesswork. We have extended Metaflow with a lightweight resource tracking tool that automatically monitors CPU, memory, GPU, and more, then recommends the most cost-effective cloud instance type for future runs. A single line of code can save you from overprovisioned costs or painful job failures!

Follow the cloud journey of a fictional company Nimbus Airlines and the process it goes through to modernize its SAP systems. This book provides a detailed guide for those looking to transition their SAP systems to the cloud using Amazon Web Services (AWS). Through the lens of various characters, the book is structured in three parts — starting with an introduction to SAP and AWS fundamentals, followed by technical architecture insights, and concluding with migration strategies and case studies, the book covers technical aspects of modernizing SAP with AWS. You’ll review the partnership between SAP and AWS, highlighted by their long-standing collaboration and shared innovations. Then design an AWS architecture tailored for SAP workloads, including high availability, disaster recovery, and operations automation. The book concludes with a tour of the migration process, offering various strategies, tools, and frameworks reinforced with real-world customer case studies that showcase successful SAP migrations to AWS. Modernizing SAP with AWS equips business leaders and technical architects with the knowledge to leverage AWS for their SAP systems, ensuring a smooth transition and unlocking new opportunities for innovation. What You Will Learn Understand the fundamentals of AWS and its key components, including computing, storage, networking, and microservices, for SAP systems. Explore the technical partnership between SAP and AWS, learning how their collaboration drives innovation and delivers business value. Design an optimized AWS architecture for SAP workloads, focusing on high availability, disaster recovery, and operations automation. Discover innovative ways to enhance and extend SAP functionality using AWS tools for better system performance and automation. Who This Book Is For SAP professionals and consultants interested in learning how AWS can enhance SAP performance, security, and automation. Cloud engineers and developers involved in SAP migration projects, looking for best practices and real-world case studies for successful implementation. Enterprise architects seeking to design optimized, scalable, and secure SAP infrastructure on AWS. CIOs, CTOs, and IT managers aiming to modernize SAP systems and unlock innovation through cloud technology.

It’s no secret that AI is reliant on ‘rock solid’ data. However given the vast amounts of data that companies now have spread across a distributed SaaS, on-premises and multi-cloud data estate, many companies they are a million miles away from this. We are also well past the point where people can govern data on their own. They need help and a total rethink is now needed to conquer data complexity and create a high quality, compliant data foundation for AI Success.

In this watershed keynote, conference char Mike Ferguson details what needs to be done to govern data in the era of AI, how companies can conquer the complexity they face, by implementing an always on, active and unified approach to data governance to continuously detect, automate and consistently enforce multiple types of policies across a distributed data estate. The session will cover:

• Current problems with data governance today and why old approaches are broken

• Requirements to dramatically improve data governance using AI and AI automation

• The need for an integrated and unified data governance platform

• Why a data catalog, data intelligence, data observability, AI Agents and orchestration all need to be integrated for AI-Assisted active data governance

• Understanding the AI-assisted data governance services and AI-Agents you need

• Establishing health metrics to measure effectiveness of your data governance program

• Creating a Data Governance Action Framework for your enterprise

• Monitoring the health and security of your data using data governance observability

• Enabling continuous reporting and AI-Assisted data governance action automation

• Implementing data governance AI Agents for different data governance disciplines

A session featuring Patrick Diebold and Ricardo Aveledo from BASF Agricultural Solutions discussing real-time, centralized visibility into BASF's application portfolio, including multi-cloud environments and critical business KPIs.

This talk, presented by Dan Keeley, Principal Data Engineer and Jonathan Conn, Digital Technology Director from England Rugby, delves into the real-world challenges and triumphs of a complex cloud-to-cloud migration.

SAP Business Data Cloud is a fully managed solution that unifies and governs all SAP data while seamlessly integrating with third-party sources. With SAP Business Data Cloud, organisations can accelerate decision-making by empowering business users to make more impactful choices. It also provides a trusted foundation for AI, ensuring that data across applications and operations is reliable, responsible, and relevant—enabling organisations to harness the full potential of generative AI.

Join Sami Hero and Tammie Coles, as they share how Ellie is reinventing data modeling with AI-native tools that empower both technical and non-technical users. With CData Embedded Cloud, Ellie brings live metadata and data models from systems like Snowflake, Databricks, and Oracle Financials into a unified modeling workspace. Their platform translates legacy structures into human-readable insights, letting users interact with a copilot-style assistant to discover, refine, and maintain data models faster—with less reliance on analysts.

You’ll see how Ellie uses generative AI to recommend new entities, reconcile differences between models and live systems, and continuously document evolving data environments. Learn how corporations are using Ellie and CData together to scale high-quality data modeling across teams. reducing rework, accelerating delivery of analytics-ready models, and making enterprise architecture accessible to the business.

The Generative AI revolution is here, but so is the operational headache. For years, teams have matured their MLOps practices for traditional models, but the rapid adoption of LLMs has introduced a parallel, often chaotic, world of LLMOps. This results in fragmented toolchains, duplicated effort, and a state of "Ops Overload" that slows down innovation.

This session directly confronts this challenge. We will demonstrate how a unified platform like Google Cloud's Vertex AI can tame this complexity by providing a single control plane for the entire AI lifecycle.

In this talk, we will introduce Ordeq, a cutting-edge data pipeline development framework used by data engineers, scientists and analysts across ING. Ordeq helps you modularise pipeline logic and abstract IO, elevating projects from proof-of-concepts to maintainable production-level applications. We will demonstrate how Ordeq integrates seamlessly with popular data processing tools like Spark, Polars, Matplotlib, DSPy, and orchestration tools such as Airflow. Additionally, we showcase how you can leverage Ordeq on public cloud offering like GCP. Ordeq has 0 dependencies and is available under MIT license.

The world has never been more connected. Today, customers demand near-perfect uptime, responsive networks, and personalized digital experiences from their telecommunications providers. 

The industry has reached an inflection point. Legacy architectures, fragmented customer data, and batch-based analytics are no longer sufficient. Now is the time for Telcos to embrace real-time, when the speed of insights and the ability to remain agile determine competitive advantage.

In this session, leaders from Orange Belgium, Google Cloud, and Striim explore how telcos can rethink their data foundations to become real-time, intelligence-driven enterprises. From centralizing data in BigQuery and Spanner to enabling dynamic customer engagement and scalable operations, Orange Belgium shares how its cloud-first strategy is enabling agility, trust, and innovation.

This isn’t just a story of technology migration—it’s about building a data culture that prioritizes immediacy, empathy, and evolution. Join us for a forward-looking conversation on how telcos can align infrastructure, intelligence, and customer intent.