Are you ready to unlock the full power of Unity Catalog managed tables? This session delivers actionable insights for transitioning to UC managed tables. Learn why managed tables are the default for performance and ease of use, and how automatic feature upgrades future-proof your architecture. Whether you manage thousands of tables or want to streamline operations, you’ll gain the tools and strategies to thrive in the era of intelligent data management. Join us and discover how easy it is to move to UC managed tables!

Supercharge advanced analytics and AI insights on Databricks with accurate and consistent master data. This session explores how Informatica’s Master Data Management (MDM) integrates with Databricks to provide high-quality, integrated golden record data like customer, supplier, product 360 or reference data to support downstream analytics, Generative AI and Agentic AI. Enterprises can accelerate and de-risk the process of creating a golden record via a no-code/low-code interface, allowing data teams to quickly integrate siloed data and create a complete and consistent record that improves decision-making speed and accuracy.

Summary In this episode of the Data Engineering Podcast Alex Albu, tech lead for AI initiatives at Starburst, talks about integrating AI workloads with the lakehouse architecture. From his software engineering roots to leading data engineering efforts, Alex shares insights on enhancing Starburst's platform to support AI applications, including an AI agent for data exploration and using AI for metadata enrichment and workload optimization. He discusses the challenges of integrating AI with data systems, innovations like SQL functions for AI tasks and vector databases, and the limitations of traditional architectures in handling AI workloads. Alex also shares his vision for the future of Starburst, including support for new data formats and AI-driven data exploration tools.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData 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.This is a pharmaceutical Ad for Soda Data Quality. Do you suffer from chronic dashboard distrust? Are broken pipelines and silent schema changes wreaking havoc on your analytics? You may be experiencing symptoms of Undiagnosed Data Quality Syndrome — also known as UDQS. Ask your data team about Soda. With Soda Metrics Observability, you can track the health of your KPIs and metrics across the business — automatically detecting anomalies before your CEO does. It’s 70% more accurate than industry benchmarks, and the fastest in the category, analyzing 1.1 billion rows in just 64 seconds. And with Collaborative Data Contracts, engineers and business can finally agree on what “done” looks like — so you can stop fighting over column names, and start trusting your data again.Whether you’re a data engineer, analytics lead, or just someone who cries when a dashboard flatlines, Soda may be right for you. Side effects of implementing Soda may include: Increased trust in your metrics, reduced late-night Slack emergencies, spontaneous high-fives across departments, fewer meetings and less back-and-forth with business stakeholders, and in rare cases, a newfound love of data. Sign up today to get a chance to win a $1000+ custom mechanical keyboard. Visit dataengineeringpodcast.com/soda to sign up and follow Soda’s launch week. It starts June 9th. This episode is brought to you by Coresignal, your go-to source for high-quality public web data to power best-in-class AI products. Instead of spending time collecting, cleaning, and enriching data in-house, use ready-made multi-source B2B data that can be smoothly integrated into your systems via APIs or as datasets. With over 3 billion data records from 15+ online sources, Coresignal delivers high-quality data on companies, employees, and jobs. It is powering decision-making for more than 700 companies across AI, investment, HR tech, sales tech, and market intelligence industries. A founding member of the Ethical Web Data Collection Initiative, Coresignal stands out not only for its data quality but also for its commitment to responsible data collection practices. Recognized as the top data provider by Datarade for two consecutive years, Coresignal is the go-to partner for those who need fresh, accurate, and ethically sourced B2B data at scale. Discover how Coresignal's data can enhance your AI platforms. Visit dataengineeringpodcast.com/coresignal to start your free 14-day trial.Your host is Tobias Macey and today I'm interviewing Alex Albu about how Starburst is extending the lakehouse to support AI workloadsInterview IntroductionHow did you get involved in the area of data management?Can you start by outlining the interaction points of AI with the types of data workflows that you are supporting with Starburst?What are some of the limitations of warehouse and lakehouse systems when it comes to supporting AI systems?What are the points of friction for engineers who are trying to employ LLMs in the work of maintaining a lakehouse environment?Methods such as tool use (exemplified by MCP) are a means of bolting on AI models to systems like Trino. What are some of the ways that is insufficient or cumbersome?Can you describe the technical implementation of the AI-oriented features that you have incorporated into the Starburst platform?What are the foundational architectural modifications that you had to make to enable those capabilities?For the vector storage and indexing, what modifications did you have to make to iceberg?What was your reasoning for not using a format like Lance?For teams who are using Starburst and your new AI features, what are some examples of the workflows that they can expect?What new capabilities are enabled by virtue of embedding AI features into the interface to the lakehouse?What are the most interesting, innovative, or unexpected ways that you have seen Starburst AI features used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI features for Starburst?When is Starburst/lakehouse the wrong choice for a given AI use case?What do you have planned for the future of AI on Starburst?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 StarburstPodcast EpisodeAWS AthenaMCP == Model Context ProtocolLLM Tool UseVector EmbeddingsRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeStarburst Data ProductsLanceLanceDBParquetORCpgvectorStarburst IcehouseThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Life as a father, tech leader, and fitness enthusiast demands efficiency. To reclaim my time, I’ve built AI-driven solutions that automate everyday tasks—from research agents that prep for podcasts to multi-agent systems that plan meals—all powered by real-time data and automation. This session dives into the technical foundations of these solutions, focusing on event-driven agent design and scalable patterns for robust AI systems. You’ll discover how Databricks technologies like Delta Lake, for reliable and scalable data management, and DSPy, for streamlining the development of generative AI workflows, empower seamless decision-making and deliver actionable insights. Through detailed architecture diagrams and a live demo, I’ll showcase how to design systems that process data in motion to tackle complex, real-world problems. Whether you’re an engineer, architect, or data scientist, you’ll leave with practical strategies to integrate AI-driven automation into your workflows.

Master Data Management (MDM) is the foundation of a successful enterprise data strategy — delivering consistency, accuracy and trust across all systems that depend on reliable data. But how can organizations integrate trusted third-party data to enhance their MDM frameworks? How can they ensure that this master data is securely and efficiently shared across internal platforms and external ecosystems? This session explores how Dun & Bradstreet’s pre-mastered data serves as a single source of truth for customers, suppliers and vendors — reducing duplication and driving alignment across enterprise systems. With Delta Sharing, organizations can natively ingest Dun & Bradstreet data into their Databricks environment and establish a scalable, interoperable MDM framework. Delta Sharing also enables secure, real-time distribution of master data across the enterprise ensuring that every system operates from a consistent and trusted foundation.

Join us to hear about how Epsilon Data Management migrated Epsilon’s unique, AI-powered marketing identity solution from multi-petabyte on-prem Hadoop and data warehouse systems to a unified Databricks Lakehouse platform. This transition enabled Epsilon to further scale its Decision Sciences solution and enable new cloud-based AI research capabilities on time and within budget, without being bottlenecked by the resource constraints of on-prem systems. Learn how Delta Lake, Unity Catalog, MLflow and LLM endpoints powered massive data volume, reduced data duplication, improved lineage visibility, accelerated Data Science and AI, and enabled new data to be immediately available for consumption by the entire Epsilon platform in a privacy-safe way. Using the Databricks platform as the base for AI and Data Science at global internet scale, Epsilon deploys marketing solutions across multiple cloud providers and multiple regions for many customers.

Deloitte is observing a growing trend among cybersecurity organizations to develop big data management and analytics solutions beyond traditional Security Information and Event Management (SIEM) systems. Leveraging Databricks to extend these SIEM capabilities, Deloitte can help clients lower the cost of cyber data management while enabling scalable, cloud-native architectures. Deloitte helps clients design and implement cybersecurity data meshes, using Databricks as a foundational data lake platform to unify and govern security data at scale. Additionally, Deloitte extends clients’ cybersecurity capabilities by integrating advanced AI and machine learning solutions on Databricks, driving more proactive and automated cybersecurity solutions. Attendees will gain insight into how Deloitte is utilizing Databricks to manage enterprise cyber risks and deliver performant and innovative analytics and AI insights that traditional security tools and data platforms aren’t able to deliver.

The ability to enforce data management controls at scale and reduce the effort required to manage data pipelines is critical to operating efficiently. Capital One has scaled its data management capabilities and invested in platforms to help address this need. In the past couple of years, the role of “the catalog” in a data platform architecture has transitioned from just providing SQL to providing a full suite of capabilities that can help solve this problem at scale. This talk will give insight into how Capital One is thinking about leveraging Databricks Unity Catalog to help tackle these challenges.

This session is repeated. In an era of exponential data growth, organizations across industries face common challenges in transforming raw data into actionable insights. This presentation showcases how Novo Nordisk is pioneering insights generation approaches to clinical data management and AI. Using our clinical trials platform FounData, built on Databricks, we demonstrate how proper data architecture enables advanced AI applications. We'll introduce a multi-agent AI framework that revolutionizes data interaction, combining specialized AI agents to guide users through complex datasets. While our focus is on clinical data, these principles apply across sectors – from manufacturing to financial services. Learn how democratizing access to data and AI capabilities can transform organizational efficiency while maintaining governance. Through this real-world implementation, participants will gain insights on building scalable data architectures and leveraging multi-agent AI frameworks for responsible innovation.

Scaling large language models (LLMs) and multimodal architectures requires efficient data management and computational power. NVIDIA NeMo Framework Megatron-LM on Databricks is an open source solution that integrates GPU acceleration and advanced parallelism with Databricks Delta Lakehouse, streamlining workflows for pre-training and fine-tuning models at scale. This session highlights context parallelism, a unique NeMo capability for parallelizing over sequence lengths, making it ideal for video datasets with large embeddings. Through the case study of TwelveLabs’ Pegasus-1 model, learn how NeMo empowers scalable multimodal AI development, from text to video processing, setting a new standard for LLM workflows.

This session introduces ByteDance’s challenges in data management and model training, and addresses them by Magnus (enhanced Apache Iceberg) and Byted Streaming (customized Mosaic Streaming). Magnus uses Iceberg’s branch/tag to manage massive datasets/checkpoints efficiently. With enhanced metadata and a custom C++ data reader, Magnus achieves optimal sharding, shuffling and data loading. Flexible table migration, detailed metrics and built-in full-text indexes on Iceberg tables further ensure training reliability. When training with ultra-large datasets, ByteDance faced scalability and performance issues. Given Streaming's scalability in distributed training and good code structure, the team chose and customized it to resolve challenges like slow startup, high resource consumption, and limited data source compatibility. In this session, we will explore Magnus and Byted Streaming, discuss their enhancements and demonstrate how they enable efficient and robust distributed training.

At Nubank, we successfully migrated to Unity Catalog, addressing the needs of our large-scale data environment with 3k active users, over 4k notebooks and jobs and 1.1 million tables, including sensitive PII data. Our primary objectives were to enhance data governance, security and user experience.Key points: Comprehensive data access monitoring and control implementation Enhanced security measures for handling PII and sensitive data Efficient migration of 4,000+ notebooks and jobs to the new system Improved cataloging and governance for 1.1 million tables Implementation of robust access controls and permissions model Optimized user experience and productivity through centralized data management This migration significantly improved our data governance capabilities, enhanced security measures and provided a more user-friendly experience for our large user base, ultimately leading to better control and utilization of our vast data resources.

Companies need robust data management capabilities to build and deploy AI. Data needs to be easy to find, understandable, and trustworthy. And it’s even more important to secure data properly from the beginning of its lifecycle, otherwise it can be at risk of exposure during training or inference. Tokenization is a highly efficient method for securing data without compromising performance. In this session, we’ll share tips for managing high-quality, well-protected data at scale that are key for accelerating AI. In addition, we’ll discuss how to integrate visibility and optimization into your compute environment to manage the hidden cost of AI — your data.

Given that data is the new oil, it must be treated as such. Organizations that pursue greater insight into their businesses and their customers must manage, govern, protect and observe the use of the data that drives these insights in an efficient, cost-effective, compliant and auditable manner without degrading access to that data. Azure Data Lake Storage offers many features which allow customers to apply such controls and protections to their critical data assets. Understanding how these features behave, the granularity, cost and scale implications and the degree of control or protection that they apply are essential to implement a data lake that reflects the value contained within. In this session, the various data protection, governance and management capabilities available now and upcoming in ADLS will be discussed. This will include how deep integration with Azure Databricks can provide a more comprehensive, end-to-end coverage for these concerns, yielding a highly efficient and effective data governance solution.

We will explore how leveraging Databricks' Unity Catalog has accelerated our FinOps maturity, enabling us to optimize platform utilization and achieve significant cost reductions. By implementing Unity Catalog, we've gained comprehensive visibility and governance over our data assets, leading to more informed decision-making and efficient resource allocation. Learn how Corning discovered actionable insights and leveraged best practices on utilizing Unity Catalog to streamline data management, enhance financial operations and drive substantial savings within your organization.

In the contemporary landscape of data management, organizations are increasingly faced with the challenges of data segregation, governance and permission management, particularly when operating within complex structures such as holding companies with multiple subsidiaries. Unipol comprises seven subsidiary companies, each with a diverse array of workgroups, leading to a cumulative total of multiple operational groups. This intricate organizational structure necessitates a meticulous approach to data management, particularly regarding the segregation of data and the assignment of precise read-and-write permissions tailored to each workgroup. The challenge lies in ensuring that sensitive data remains protected while enabling seamless access for authorized users. This speech wants to demonstrate how Unity Catalog emerges as a pivotal tool in the daily use of the data platform, offering a unified governance solution that supports data management across diverse AWS environments.

In an era where data drives strategic decision-making, organizations must adapt to the evolving landscape of business analytics. This session will focus on three pivotal themes shaping the future of data management and analytics in 2025. Join our panel of experts, including a Business Analytics Leader, Head of Information Governance, and Data Science Leader, as they explore: - Knowledge-Powered AI: Discover trends in Knowledge-Powered AI and how these initiatives can revolutionize business analytics, with real-world examples of successful implementations. - Information Governance: Explore the role of information governance in ensuring data integrity and compliance. Our experts will discuss strategies for establishing robust frameworks that protect organizational assets. - Real-Time Analytics: Understand the importance of real-time analytics in today’s fast-paced environment. The panel will highlight how organizations can leverage real-time data for agile decision-making.

What if you could simplify data management, boost performance, and cut costs-all at once? Join us to discover how Unity Catalog managed tables can slash your storage costs, supercharge query speeds, and automate optimizations with AI on the Data Intelligence Platform. Experience seamless interoperability with third-party clients, and be among the first to preview our new game-changing tool that makes moving to UC managed tables effortless. Don’t miss this exciting session that will redefine your data strategy!

Marketing teams struggle with ‘dirty data’ — incomplete, inconsistent, and inaccurate information that limits campaign effectiveness and reduces the accuracy of AI agents. Our AI-powered marketing data management platform, built on Databricks, solves this with anomaly detection, ML-driven transformations and the built-in Acxiom Referential Real ID Graph with Data Hygiene.We’ll showcase how Delta Lake, Unity Catalog and Lakeflow Declarative Pipelines power our multi-tenant architecture, enabling secure governance and 75% faster data processing. Our privacy-first design ensures compliance with GDPR, CCPA and HIPAA through role-based access, encryption key management and fine-grained data controls.Join us for a live demo and Q&A, where we’ll share real-world results and lessons learned in building a scalable, AI-driven marketing data solution with Databricks.

In this course, you'll learn concepts and perform labs that showcase workflows using Unity Catalog - Databricks' unified and open governance solution for data and AI. We'll start off with a brief introduction to Unity Catalog, discuss fundamental data governance concepts, and then dive into a variety of topics including using Unity Catalog for data access control, managing external storage and tables, data segregation, and more. Pre-requisites: Beginner familiarity with the Databricks Data Intelligence Platform (selecting clusters, navigating the Workspace, executing notebooks), cloud computing concepts (virtual machines, object storage, etc.), production experience working with data warehouses and data lakes, intermediate experience with basic SQL concepts (select, filter, groupby, join, etc), beginner programming experience with Python (syntax, conditions, loops, functions), beginner programming experience with the Spark DataFrame API (Configure DataFrameReader and DataFrameWriter to read and write data, Express query transformations using DataFrame methods and Column expressions, etc.) Labs: Yes Certification Path: Databricks Certified Data Engineer Associate