Our data platform journey started with a classic data lake — easy to ingest, hard to evolve. As domains scaled, tight coupling across source systems, pipelines, and data products slowed everything down. In this talk, we share how we re-architected toward a domain-oriented data mesh using PySpark, Delta Lake and DQX to achieve true decoupling. Expect practical lessons on designing independent data products, managing lineage and governance, and scaling self-service without chaos.

Aura Minerals cut pipeline migration time by 87%, transforming a 45-hour manual process into a 6-hour lift with relatively little manual oversight. In this session, learn how the team used AI tools to accelerate their shift from a complex PySpark environment to a streamlined, modern dbt workflow. You’ll walk away with a clear view of the strategy, the tools they used, and what it looks like to modernize your data stack with AI as a force multiplier.

How to use LLMs to categorize hundreds of thousands of products into 1,000 categories at scale? Learn about our journey from manual/rule-based methods, via fine-tuned semantic models, to a robust multi-step process which uses embeddings and LLMs via the OpenAI APIs. This talk offers data scientists and AI practitioners learnings and best practices for putting such a complex LLM-based system into production. This includes prompt development, balancing cost vs. accuracy via model selection, testing mult-case vs. single-case prompts, and saving costs by using the OpenAI Batch API and a smart early-stopping approach. We also describe our automation and monitoring in a PySpark environment.

Ever tried passing a Polars Dataframe to a data science library and found that it...just works? No errors, no panics, no noticeable overhead, just...results? This is becoming increasingly common in 2025, yet only 2 years ago, it was mostly unheard of. So, what changed? A large part of the answer is: Narwhals.

Narwhals is a lightweight compatibility layer between dataframe libraries which lets your code work seamlessly across Polars, pandas, PySpark, DuckDB, and more! And it's not just a theoretical possibility: with ~30 million monthly downloads and set as a required dependency of Altair, Bokeh, Marimo, Plotly, Shiny, and more, it's clear that it's reshaping the data science landscape. By the end of the talk, you'll understand why writing generic dataframe code was such a headache (and why it isn't anymore), how Narwhals works and how its community operates, and how you can use it in your projects today. The talk will be technical yet accessible and light-hearted.

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.

Suppose you want to write a data science tool to do feature engineering. Your experience may go like this: - Expectation: you can focus on state-of-the art techniques for feature engineering. - Reality: you keep having to make you codebase more complex because a new dataframe library has come out and users are demanding support for it.

Or rather, it might have gone like that in the pre-Narwhals era. Because now, you can focus on solving the problems which your tool set out to do, and let Narwhals handle the subtle differences between different kinds of dataframe inputs!

Reynold Xin explains the evolution of Apache Spark™, outlining several historical challenges and how the Spark community worked to make improvements, including the addition of PySpark.

Speaker: Reynold Xin, Co-founder and Chief Architect at Databricks

Now that you can write models in Python, a new world of possibility has opened up. In this session, Christopher Marland introduces you to Snowpark and how it integrates with dbt, before demonstrating a real-world use case where Python transformations outperform SQL, starting from raw data and moving through to a completed analysis.

This talk is ideal for people who are familiar with PySpark but new to dbt, or who are experienced dbt users and curious about taking advantage of their new Pythonic superpowers from inside of a familiar development environment.

Speaker: Christopher Marland, Snowflake Solutions Architect, Aimpoint Digital

Register for Coalesce at https://coalesce.getdbt.com

Legacy data warehouses are costly to maintain, unscalable and cannot deliver on data science, ML and real-time analytics use cases. Migrating from your enterprise data warehouse to Databricks lets you scale as your business needs grow and accelerate innovation by running all your data, analytics and AI workloads on a single unified data platform.

In the first part of this session we will guide you through the well-designed process and tools that will help you from the assessment phase to the actual implementation of an EDW migration project. Also, we will address ways to convert PL/SQL proprietary code to an open standard python code and take advantage of PySpark for ETL workloads and Databricks SQL’s data analytics workload power.

The second part of this session will be based on an EDW migration project of SNCF (French national railways); one of the major enterprise customers of Databricks in France. Databricks partnered with SNCF to migrate its real estate entity from Oracle DW and IBM DataStage to Databricks on AWS. We will walk you through the customer context, urgency to migration, challenges, target architecture, nitty-gritty details of implementation, best practices, recommendations, and learnings in order to execute a successful migration project in a very accelerated time frame.

Talk by: Himanshu Arora and Amine Benhamza

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Apache Spark is a popular distributed framework for big data processing. It is commonly used for ETL (extract, transform and load) across large datasets. Today, the transform stage can often include the application of deep learning models on the data. For example, common models can be used for classification of images, sentiment analysis of text, language translation, anomaly detection, and many other use cases. Applying these models within Spark can be done today with the combination of PySpark, Pandas_UDF, and a lot of glue code. Often, that glue code can be difficult to get right, because it requires expertise across multiple domains - deep learning frameworks, PySpark APIs, pandas_UDF internal behavior, and performance optimization.

In this session, we introduce a new, simplified API for deep learning inferencing on Spark, introduced in SPARK-40264 as a collaboration between NVIDIA and Databricks, which seeks to standardize and open source this glue code to make deep learning inference integrations easier for everyone. We discuss its design and demonstrate its usage across multiple deep learning frameworks and models.

Talk by: Lee Yang

Here’s more to explore: LLM Compact Guide: https://dbricks.co/43WuQyb Big Book of MLOps: https://dbricks.co/3r0Pqiz

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

PySpark has accomplished many milestones such as Project Zen, and been increasingly growing. We introduced pandas API on Spark, and hugely improved usability such as error messages, type hints, etc., and PySpark has become almost the very standard of distributed computing in Python. With this trend, the kind of PySpark use cases became also very complicated especially for modern data applications such as notebooks, IDEs, even devices such as smart home devices leveraging the power of data, that virtually need a lightweight separate client. However, today’s PySpark client is considerably heavy, and does not allow the separation from its scheduler, optimizer and analyzer as an example.

In Apache Spark 3.4, one of the key features we introduced in PySpark is the Python client for Spark Connect that decouples client-server architecture for Apache Spark that allows remote connectivity to Spark clusters using the DataFrame API and unresolved logical plans as the protocol. The separation between client and server allows Apache Spark and its open ecosystem to be leveraged from everywhere. It can be embedded in modern data applications. In this talk, we will introduce what Spark Connect is, the internals of Spark Connect with Python, how to use Spark Connect with Python in the end-user perspective, and what’s next beyond Apache Spark 3.4.

Talk by: Hyukjin Kwon and Ruifeng Zheng

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Since the release of Delta 2.0, the project has been growing at a breakneck speed. In this session, we will cover all the latest capabilities that makes Delta Lake the best format for the lakehouse. Based on lessons learned from this past year, we will introduce Project Aqueduct and how we will simplify building Delta Lake APIs from Rust and Go to Trino, Flink, and PySpark.

Talk by: Tathagata Das and Denny Lee

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc