We partnered with Databricks to pioneer a new standard in financial sector's enterprise AI, balancing rapid AI democratization with strict regulatory and security requirements. At the core is our Responsible AI Gateway, enforcing jailbreak prevention and compliance on every LLM query. Real-time observability, powered by Databricks, calculates risk and accuracy metrics, detecting issues before escalation. Leveraging Databricks' model hosting ensures scalable LLM access, fortifying security and efficiency. We built frameworks to democratize AI without compromising guardrails. Operating in a regulated environment, we showcase how Databricks enables democratization and responsible AI at scale, offering best practices for financial organizations to harness AI safely and efficiently.

The next era of data transformation has arrived. AI is enhancing developer workflows, enabling downstream teams to collaborate effectively through governed self-service. Additionally, SQL comprehension is producing detailed metadata that boosts developer efficiency while ensuring data quality and cost optimization. Experience this firsthand with dbt’s data control plane, a centralized platform that provides organizations with repeatable, scalable, and governed methods to succeed with Databricks in the modern age.

Managing authentication effectively is key to securing your data platform. In this session, we’ll explore best practices from Databricks for overcoming authentication challenges, including token visibility, MFA/SSO, CI/CD token federation and risk containment. Discover how to map your authentication maturity journey while maximizing security ROI. We'll showcase new capabilities like access token reports for improved visibility, streamlined MFA implementation and secure SSO with token federation. Learn strategies to minimize token risk through TTL limits, scoped tokens and network policies. You'll walk away with actionable insights to enhance your authentication practices and strengthen platform security on Databricks.

Hear from VC leaders, startup founders and early stage customers building on Databricks around what they are seeing in the market and how they are scaling their early stage companies on Databricks. This event is a must see for VCs, founders and those interested in the early stage company ecosystem.

This session is repeated. Did you know that you can integrate with your favorite BI tools directly from Databricks SQL? You don’t even need to stand up an additional warehouse. This session shows the integrations with Microsoft Power Platform, Power BI, Tableau and dbt so you can have a seamless integration experience. Directly connect your Databricks workspace with Fabric and Power BI workspaces or Tableau to publish and sync data models, with defined primary and foreign keys, between the two platforms.

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.

This session is repeated. Want to accelerate your team's data science workflow? This session reveals how Databricks Notebooks can transform your productivity through an optimized environment designed specifically for data science and AI work. Discover how notebooks serve as a central collaboration hub where code, visualizations, documentation and results coexist seamlessly, enabling faster iteration and development. Key takeaways: Leveraging interactive coding features including multi-language support, command-mode shortcuts and magic commands Implementing version control best practices through Git integration and notebook revision history Maximizing collaboration through commenting, sharing and real-time co-editing capabilities Streamlining ML workflows with built-in MLflow tracking and experiment management You'll leave with practical techniques to enhance your notebook-based workflow and deliver AI projects faster with higher-quality results.

This session is repeated.Databricks Asset Bundles (DABs) provide a way to use the command line to deploy and run a set of Databricks assets — like notebooks, Python code, Lakeflow Declarative Pipelines and workflows. To automate deployments, you create a deployment pipeline that uses the power of DABs along with other validation steps to ensure high quality deployments.In this session you will learn how to automate CI/CD processes for Databricks while following best practices to keep deployments easy to scale and maintain. After a brief explanation of why Databricks Asset Bundles are a good option for CI/CD, we will walk through a working project including advanced variables, target-specific overrides, linting, integration testing and automatic deployment upon code review approval. You will leave the session clear on how to build your first GitHub Action using DABs.ub Action using DABs.

Databricks is the bestest platform ever where everything is perfect and nothing else could ever make it any better, right? …right? You and I know, this is not true. Don’t get me wrong, there are features that I absolutely love, but there are also some that require powering through the papercuts. And then there are those that I pretend don’t exist. I’ll be opening up to give my honest take on three of each category, why I do (or don’t) like them, and then telling you which talks to attend to find out more.

You shouldn’t have to sacrifice data governance just to leverage the tools your business needs. In this session, we will give practical tips on how you can cut through the data sprawl and get a unified view of your data estate in Unity Catalog without disrupting existing workloads. We will walk through how to set up federation with Glue, Hive Metastore, and other catalogs like Snowflake, and show you how powerful new tools help you adopt Databricks at your own pace with no downtime and full interoperability.

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

In this course, you’ll learn how to orchestrate data pipelines with Lakeflow Jobs (previously Databricks Workflows) and schedule dashboard updates to keep analytics up-to-date. We’ll cover topics like getting started with Lakeflow Jobs, how to use Databricks SQL for on-demand queries, and how to configure and schedule dashboards and alerts to reflect updates to production data pipelines. 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: No Certification Path: Databricks Certified Data Engineer Associate

In this session, we will explore effective strategies for optimizing costs on the Databricks platform, a leading solution for handling large-scale data workloads. Databricks, known for its open and unified approach, offers several tools and methodologies to ensure users can maximize their return on investment (ROI) while managing expenses efficiently. Key points: Understanding usage with AI/BI tools Organizing costs with tagging Setting up budgets Leveraging System Tables By the end of this session, you will have a comprehensive understanding of how to leverage Databricks' built-in tools for cost optimization, ensuring that their data and AI projects not only deliver value but do so in a cost-effective manner. This session is ideal for data engineers, financial analysts, and decision-makers looking to enhance their organization’s efficiency and financial performance through strategic cost management on Databricks.

Join us for an introductory session on Databricks DQX, a Python-based framework designed to validate the quality of PySpark DataFrames. Discover how DQX can empower you to proactively tackle data quality challenges, enhance pipeline reliability and make more informed business decisions with confidence. Traditional data quality tools often fall short by providing limited, actionable insights, relying heavily on post-factum monitoring, and being restricted to batch processing. DQX overcomes these limitations by enabling real-time quality checks at the point of data entry, supporting both batch and streaming data validation and delivering granular insights at the row and column level. If you’re seeking a simple yet powerful data quality framework that integrates seamlessly with Databricks, this session is for you.

This course introduces learners to evaluating and governing GenAI (generative artificial intelligence) systems. First, learners will explore the meaning behind and motivation for building evaluation and governance/security systems. Next, the course will connect evaluation and governance systems to the Databricks Data Intelligence Platform. Third, learners will be introduced to a variety of evaluation techniques for specific components and types of applications. Finally, the course will conclude with an analysis of evaluating entire AI systems with respect to performance and cost. Pre-requisites: Familiarity with prompt engineering, and experience with the Databricks Data Intelligence Platform. Additionally, knowledge of retrieval-augmented generation (RAG) techniques including data preparation, embeddings, vectors, and vector databases Labs: Yes Certification Path: Databricks Certified Generative AI Engineer Associate

Hundreds of customers are already ingesting data with Lakeflow Connect from SQL Server, Salesforce, ServiceNow, Google Analytics, SharePoint, PostgreSQL and more to unlock the full power of their data. Lakeflow Connect introduces built-in, no-code ingestion connectors from SaaS applications, databases and file sources to help unlock data intelligence. In this demo-packed session, you’ll learn how to ingest ready-to-use data for analytics and AI with a few clicks in the UI or a few lines of code. We’ll also demonstrate how Lakeflow Connect is fully integrated with the Databricks Data Intelligence Platform for built-in governance, observability, CI/CD, automated pipeline maintenance and more. Finally, we’ll explain how to use Lakeflow Connect in combination with downstream analytics and AI tools to tackle common business challenges and drive business impact.

A big challenge in LLM development and synthetic data generation is ensuring data quality and diversity. While data incorporating varied perspectives and reasoning traces consistently improves model performance, procuring such data remains impossible for most enterprises. Human-annotated data struggles to scale, while purely LLM-based generation often suffers from distribution clipping and low entropy. In a novel compound AI approach, we combine LLMs with probabilistic graphical models and other tools to generate synthetic personas grounded in real demographic statistics. The approach allows us to address major limitations in bias, licensing, and persona skew of existing methods. We release the first open-source dataset aligned with real-world distributions and show how enterprises can leverage it with Gretel Data Designer (now part of NVIDIA) to bring diversity and quality to model training on the Databricks platform, all while addressing model collapse and data provenance concerns head-on.

Auto Loader is the definitive tool for ingesting data from cloud storage into your lakehouse. In this session, we’ll unveil new features and best practices that simplify every aspect of cloud storage ingestion. We’ll demo out-of-the-box observability for pipeline health and data quality, walk through improvements for schema management, introduce a series of new data formats and unveil recent strides in Auto Loader performance. Along the way, we’ll provide examples and best practices for optimizing cost and performance. Finally, we’ll introduce a preview of what’s coming next — including a REST API for pushing files directly to Delta, a UI for creating cloud storage pipelines and more. Join us to help shape the future of file ingestion on Databricks.

This course is designed to introduce three primary machine learning deployment strategies and illustrate the implementation of each strategy on Databricks. Following an exploration of the fundamentals of model deployment, the course delves into batch inference, offering hands-on demonstrations and labs for utilizing a model in batch inference scenarios, along with considerations for performance optimization. The second part of the course comprehensively covers pipeline deployment, while the final segment focuses on real-time deployment. Participants will engage in hands-on demonstrations and labs, deploying models with Model Serving and utilizing the serving endpoint for real-time inference. By mastering deployment strategies for a variety of use cases, learners will gain the practical skills needed to move machine learning models from experimentation to production. This course shows you how to operationalize AI solutions efficiently, whether it's automating decisions in real-time or integrating intelligent insights into data pipelines. Pre-requisites: Familiarity with Databricks workspace and notebooks, familiarity with Delta Lake and Lakehouse, intermediate level knowledge of Python (e.g. common Python libraries for DS/ML like Scikit-Learn, awareness of model deployment strategies) Labs: Yes Certification Path: Databricks Certified Machine Learning Associate

In this session, attendees will learn how to leverage Databricks' system tables to measure user adoption and track key performance indicators (KPIs) for data products. The session will focus on how organizations can use system tables to analyze user behavior, assess engagement with data products and identify usage trends that can inform product development. By measuring KPIs such as user retention, frequency of use and data queries, organizations can optimize their data products for better performance and ROI.