PySpark supports many data sources out of the box, such as Apache Kafka, JDBC, ODBC, Delta Lake, etc. However, some older systems, such as systems that use JMS protocol, are not supported by default and require considerable extra work for developers to read from them. One such example is ActiveMQ for streaming. Traditionally, users of ActiveMQ have to use a middle-man in order to read the stream with Spark (such as writing to a MySQL DB using Java code and reading that table with Spark JDBC). With PySpark 4.0’s custom data sources (supported in DBR 15.3+) we are able to cut out the middle-man processing using batch or Spark Streaming and consume the queues directly from PySpark, saving developers considerable time and complexity in getting source data into your Delta Lake and governed by Unity Catalog and orchestrated with Databricks Workflows.

Migrating your legacy Oracle data warehouse to the Databricks Data Intelligence Platform can accelerate your data modernization journey. In this session, learn the top strategies for completing this data migration. We will cover data type conversion, basic to complex code conversions, validation and reconciliation best practices. Discover the pros and cons of using CSV files to PySpark or using pipelines to Databricks tables. See before-and-after architectures of customers who have migrated, and learn about the benefits they realized.

Eli Lilly and Company, a leading bio-pharma company, is revolutionizing manufacturing with next-gen fully digital sites. Lilly and Tredence have partnered to establish a Databricks-powered Global Manufacturing Data Fabric (GMDF), laying the groundwork for transformative data products used by various personas at sites and globally. By integrating data from various manufacturing systems into a unified data model, GMDF has delivered actionable insights across several use cases such as batch release by exception, predictive maintenance, anomaly detection, process optimization and more. Our serverless architecture leverages Databricks Auto Loader for real-time data streaming, PySpark for automation and Unity Catalog for governance, ensuring seamless data processing and optimization. This platform is the foundation for data driven processes, self-service analytics, AI and more. This session will provide details on the data architecture and strategy and share a few use cases delivered.

Apache Spark has long been recognized as the leading open-source unified analytics engine, combining a simple yet powerful API with a rich ecosystem and top-notch performance. In the upcoming Spark 4.1 release, the community reimagines Spark to excel at both massive cluster deployments and local laptop development. We’ll start with new single-node optimizations that make PySpark even more efficient for smaller datasets. Next, we’ll delve into a major “Pythonizing” overhaul — simpler installation, clearer error messages and Pythonic APIs. On the ETL side, we’ll explore greater data source flexibility (including the simplified Python Data Source API) and a thriving UDF ecosystem. We’ll also highlight enhanced support for real-time use cases, built-in data quality checks and the expanding Spark Connect ecosystem — bridging local workflows with fully distributed execution. Don’t miss this chance to see Spark’s next chapter!

PySpark has long been a cornerstone of big data processing, excelling in data preparation, analytics and machine learning tasks within traditional data lakes. However, the rise of multimodal AI and vector search introduces challenges beyond its capabilities. Spark’s new Python data source API enables integration with emerging AI data lakes built on the multi-modal Lance format. Lance delivers unparalleled value with its zero-copy schema evolution capability and robust support for large record-size data (e.g., images, tensors, embeddings, etc), simplifying multimodal data storage. Its advanced indexing for semantic and full-text search, combined with rapid random access, enables high-performance AI data analytics to the level of SQL. By unifying PySpark's robust processing capabilities with Lance's AI-optimized storage, data engineers and scientists can efficiently manage and analyze the diverse data types required for cutting-edge AI applications within a familiar big data framework.

At DraftKings, ensuring secure, fair gaming requires detecting fraud in real time with both speed and precision. In this talk, we’ll share how Databricks powers our fraud detection pipeline, integrating real-time streaming, machine learning and rule-based detection within a PySpark framework. Our system enables rapid model training, real-time inference and seamless feature transformation across historical and live data. We use shadow mode to test models and rules in live environments before deployment. Collaborating with Databricks, we push online feature store performance and enhance real-time PySpark capabilities. We'll cover PySpark-based feature transformations, real-time inference, scaling challenges and our migration from a homegrown system to Databricks. This session is for data engineers and ML practitioners optimizing real-time AI workloads, featuring a deep dive, code snippets and lessons from building and scaling fraud detection.

PySpark’s DataFrame API is evolving to support more expressive and modular workflows. In this session, we’ll introduce two powerful additions: table-valued functions (TVFs) and the new subquery API. You’ll learn how to define custom TVFs using Python User-Defined Table Functions (UDTFs), including support for polymorphism, and how subqueries can simplify complex logic. We’ll also explore how lateral joins connect these features, followed by practical tools for the PySpark developer experience—such as plotting, profiling, and a preview of upcoming capabilities like UDF logging and a Python-native data source API. Whether you're building production pipelines or extending PySpark itself, this talk will help you take full advantage of the latest features in the PySpark ecosystem.

Building and deploying Pyspark pipelines to Databricks should be effortless. However, our team at FreeWheel has, for the longest time, struggled with a convoluted and hard-to-maintain CI/CD infrastructure. It followed an imperative paradigm, demanding that every project implement custom scripts to build artifacts and deploy resources, and resulting in redundant boilerplate code and awkward interactions with the Databricks REST API. We set our mind on rebuilding it from scratch, following a declarative paradigm instead. We will share how we were able to eliminate thousands of lines of code from our repository, create a fully configuration-driven infrastructure where projects can be easily onboarded, and improve the quality of our codebase using Hatch and Databricks Asset Bundles as our tools of choice. In particular, DAB has made deploying across our 3 environments a breeze, and has allowed us to quickly adopt new features as soon as they are released by 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.

In this course, you’ll learn how to apply patterns to securely store and delete personal information for data governance and compliance on the Data Intelligence Platform. We’ll cover topics like storing sensitive data appropriately to simplify granting access and processing deletes, processing deletes to ensure compliance with the right to be forgotten, performing data masking, and configuring fine-grained access control to configure appropriate privileges to sensitive data.Pre-requisites: Ability to perform basic code development tasks using the Databricks workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc), intermediate programming experience with SQL and PySpark (extract data from a variety of file formats and data sources, apply a number of common transformations to clean data, reshape and manipulate complex data using advanced built-in functions), intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions etc.). Beginner experience with Lakeflow Declarative Pipelines and streaming workloads.Labs: YesCertification Path: Databricks Certified Data Engineer Professional

In this course, you’ll learn how to optimize workloads and physical layout with Spark and Delta Lake and and analyze the Spark UI to assess performance and debug applications. We’ll cover topics like streaming, liquid clustering, data skipping, caching, photons, and more. Pre-requisites: Ability to perform basic code development tasks using the Databricks workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc), intermediate programming experience with SQL and PySpark (extract data from a variety of file formats and data sources, apply a number of common transformations to clean data, reshape and manipulate complex data using advanced built-in functions), intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions etc.). Labs: Yes Certification Path: Databricks Certified Data Engineer Professional

In this course, you’ll learn how to Incrementally process data to power analytic insights with Structured Streaming and Auto Loader, and how to apply design patterns for designing workloads to perform ETL on the Data Intelligence Platform with Lakeflow Declarative Pipelines. First, we’ll cover topics including ingesting raw streaming data, enforcing data quality, implementing CDC, and exploring and tuning state information. Then, we’ll cover options to perform a streaming read on a source, requirements for end-to-end fault tolerance, options to perform a streaming write to a sink, and creating an aggregation and watermark on a streaming dataset. Pre-requisites: Ability to perform basic code development tasks using the Databricks workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc.), intermediate programming experience with SQL and PySpark (extract data from a variety of file formats and data sources, apply a number of common transformations to clean data, reshape and manipulate complex data using advanced built-in functions), intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions etc.). Beginner experience with streaming workloads and familiarity with Lakeflow Declarative Pipelines. Labs: No Certification Path: Databricks Certified Data Engineer Professional

In this course, you’ll learn the fundamentals of preparing data for machine learning using Databricks. We’ll cover topics like exploring, cleaning, and organizing data tailored for traditional machine learning applications. We’ll also cover data visualization, feature engineering, and optimal feature storage strategies. By building a strong foundation in data preparation, this course equips you with the essential skills to create high-quality datasets that can power accurate and reliable machine learning and AI models. Whether you're developing predictive models or enabling downstream AI applications, these capabilities are critical for delivering impactful, data-driven solutions. Pre-requisites: Familiarity with Databricks workspace, notebooks, as well as Unity Catalog. An intermediate level knowledge of Python (scikit-learn, Matplotlib), Pandas, and PySpark. As well as with concepts of exploratory data analysis, feature engineering, standardization, and imputation methods). Labs: Yes Certification Path: Databricks Certified Machine Learning Associate