Data engineering teams are frequently tasked with building bespoke ingest and/or egress solutions for myriad custom, proprietary, or industry-specific data sources or sinks. Many teams find this work cumbersome and time-consuming. Recognizing these challenges, Databricks interviewed numerous companies across different industries to better understand their diverse data integration needs. This comprehensive feedback led us to develop the Python Data Source API for Apache Spark™.

Five years ago, the delta-rs project embarked on a journey to bring Delta Lake's robust capabilities to the Rust & Python ecosystem. In this talk, we'll delve into the triumphs, tribulations and lessons learned along the way. We'll explore how delta-rs has matured alongside the thriving Rust data ecosystem, adapting to its evolving landscape and overcoming the challenges of maintaining a complex data project. Join us as we share insights into the project's evolution, the symbiotic relationship between delta-rs and the Rust community, and the current hurdles and future directions that lie ahead. Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

This course will guide participants through a comprehensive exploration of machine learning model operations, focusing on MLOps and model lifecycle management. The initial segment covers essential MLOps components and best practices, providing participants with a strong foundation for effectively operationalizing machine learning models. In the latter part of the course, we will delve into the basics of the model lifecycle, demonstrating how to navigate it seamlessly using the Model Registry in conjunction with the Unity Catalog for efficient model management. By the course's conclusion, participants will have gained practical insights and a well-rounded understanding of MLOps principles, equipped with the skills needed to navigate the intricate landscape of machine learning model operations. Pre-requisites: Familiarity with Databricks workspace and notebooks, familiarity with Delta Lake and Lakehouse, intermediate level knowledge of Python (e.g. understanding of basic MLOps concepts and practices as well as infrastructure and importance of monitoring MLOps solutions) Labs: Yes Certification Path: Databricks Certified Machine Learning Associate

SQL warehouses in Databricks can run more than just SQL. Join this session to learn how to get more out of your SQL warehouses and any tools built on top of it by leveraging Python. After attending this session, you will be familiar with Python user-defined functions and how to bring in custom dependencies from PyPi, as a custom wheel or even securely invoke cloud services with performance at scale.

This session is repeated.In this session, you will learn how to integrate Lakeflow Declarative Pipelines with external systems in order to ingest and send data virtually anywhere. Lakeflow Declarative Pipelines is most often used in ingestion and ETL into the Lakehouse. New Lakeflow Declarative Pipelines capabilities like the Lakeflow Declarative Pipelines Sinks API and added support for Python Data Source and ForEachBatch have opened up Lakeflow Declarative Pipelines to support almost any integration. This includes popular Apache Spark™ integrations like JDBC, Kafka, External and managed Delta tables, Azure CosmosDB, MongoDB and more.

Real-time data is one of the most important datasets for any Data and AI Platform across any industry. Spark 4.0 and Delta 4.0 include new features that make ingestion and querying of real-time data better than ever before. Features such as: Python custom data sources for simple ingestion of streaming and batch time series data sources using Spark Variant types for managing variable data types and json payloads that are common in the real time domain Delta liquid clustering for simple data clustering without the overhead or complexity of partitioning In this presentation you will learn how data teams can leverage these latest features to build industry-leading, real-time data products using Spark and Delta and includes real world examples and metrics of the improvements they make in performance and processing of data in the real time space.

Apache Spark™ has introduced Arrow-optimized APIs such as Pandas UDFs and the Pandas Functions API, providing high performance for Python workloads. Yet, many users continue to rely on regular Python UDFs due to their simple interface, especially when advanced Python expertise is not readily available. This talk introduces a powerful new feature in Apache Spark that brings Arrow optimization to regular Python UDFs. With this enhancement, users can leverage performance gains without modifying their existing UDFs — simply by enabling a configuration setting or toggling a UDF-level parameter. Additionally, we will dive into practical tips and features for using Arrow-optimized Python UDFs effectively, exploring their strengths and limitations. Whether you’re a Spark beginner or an experienced user, this session will allow you to achieve the best of both simplicity and performance in your workflows with regular Python UDFs.

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.

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

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 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

The course is designed to cover advanced concepts and workflows in machine learning operations. It starts by introducing participants to continuous integration (CI) and continuous development (CD) workflows within machine learning projects, guiding them through the deployment of a sample CI/CD workflow using Databricks in the first section. Moving on to the second part, participants delve into data and model testing, where they actively create tests and automate CI/CD workflows. Finally, the course concludes with an exploration of model monitoring concepts, demonstrating the use of Lakehouse Monitoring to oversee machine learning models in production settings. Pre-requisites: Familiarity with Databricks workspace and notebooks; knowledge of machine learning model development and deployment with MLflow (e.g. intermediate-level knowledge of traditional ML concepts, development with CI/CD, the use of Python and Git for ML projects with popular platforms like GitHub) Labs: Yes Certification Path: Databricks Certified Machine Learning Professional

In this course, you’ll learn how to define and schedule data pipelines that incrementally ingest and process data through multiple tables on the Data Intelligence Platform, using Lakeflow Declarative Pipelines in Spark SQL and Python. We’ll cover topics like how to get started with Lakeflow Declarative Pipelines, how Lakeflow Declarative Pipelines tracks data dependencies in data pipelines, how to configure and run data pipelines using the Lakeflow Declarative Pipelines. UI, how to use Python or Spark SQL to define data pipelines that ingest and process data through multiple tables on the Data Intelligence Platform, using Auto Loader and Lakeflow Declarative Pipelines, how to use APPLY CHANGES INTO syntax to process Change Data Capture feeds, and how to review event logs and data artifacts created by pipelines and troubleshoot syntax.By streamlining and automating reliable data ingestion and transformation workflows, this course equips you with the foundational data engineering skills needed to help kickstart AI use cases. Whether you're preparing high-quality training data or enabling real-time AI-driven insights, this course is a key step in advancing your AI journey.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: NoCertification Path: Databricks Certified Data Engineer Associate

In this course, you’ll learn how to develop traditional machine learning models on Databricks. We’ll cover topics like using popular ML libraries, executing common tasks efficiently with AutoML and MLflow, harnessing Databricks' capabilities to track model training, leveraging feature stores for model development, and implementing hyperparameter tuning. Additionally, the course covers AutoML for rapid and low-code model training, ensuring that participants gain practical, real-world skills for streamlined and effective machine learning model development in the Databricks environment. 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, fundamental ML algorithms like regression and classification, model evaluation with common metrics) Labs: Yes Certification Path: Databricks Certified Machine Learning Associate

In this course, you’ll learn how to have efficient data ingestion with Lakeflow Connect and manage that data. Topics include ingestion with built-in connectors for SaaS applications, databases and file sources, as well as ingestion from cloud object storage, and batch and streaming ingestion. We'll cover the new connector components, setting up the pipeline, validating the source and mapping to the destination for each type of connector. We'll also cover how to ingest data with Batch to Streaming ingestion into Delta tables, using the UI with Auto Loader, automating ETL with Lakeflow Declarative Pipelines or using the API.This will prepare you to deliver the high-quality, timely data required for AI-driven applications by enabling scalable, reliable, and real-time data ingestion pipelines. Whether you're supporting ML model training or powering real-time AI insights, these ingestion workflows form a critical foundation for successful AI implementation.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: NoCertification Path: Databricks Certified Data Engineer Associate

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

The course intends to equip professional-level machine learning practitioners with knowledge and hands-on experience in utilizing Apache Spark™ for machine learning purposes, including model fine-tuning. Additionally, the course covers using the Pandas library for scalable machine learning tasks. The initial section of the course focuses on comprehending the fundamentals of Apache Spark™ along with its machine learning capabilities. Subsequently, the second section delves into fine-tuning models using the hyperopt library. The final segment involves learning the implementation of the Pandas API within Apache Spark™, encompassing guidance on Pandas UDFs (User-Defined Functions) and the Functions API for model inference. Pre-requisites: Familiarity with Databricks workspace and notebooks; knowledge of machine learning model development and deployment with MLflow (e.g. basic understanding of DS/ML concepts, common model metrics and python libraries as well as a basic understanding of scaling workloads with Spark) Labs: Yes Certification Path: Databricks Certified Machine Learning Professional

Ray is an open-source framework for scaling Python applications, particularly machine learning and AI workloads. It provides the layer for parallel processing and distributed computing. Many large language models (LLMs), including OpenAI's GPT models, are trained using Ray.

On the other hand, Apache Airflow is a consolidated data orchestration framework downloaded more than 20 million times monthly.

This talk presents the Airflow Ray provider package that allows users to interact with Ray from an Airflow workflow. In this talk, I'll show how to use the package to create Ray clusters and how Airflow can trigger Ray pipelines in those clusters.

If you or your organization are spending time and resources attending a Python conference, you will want to ensure your team gets something immediately actionable and helpful out of it. As coders, we often think about writing code as the only way to contribute. However, pull request reviews are an often overlooked, but highly actionable way to have an impact.

Giving good PR reviews is an art, with two equally important parts: the technical side and the communication side. While the technical side ensures the quality, maintainability, and efficiency of the Python code, the communication around the PR determines whether the feedback can be understood and acted upon. However, we have all seen code reviews that have been ignored or executed poorly due to poor communication.

This talk addresses both facets of PR reviews by introducing the archetypes of bad code reviewers: 1) The “Looks Good to Me” Reviewer: This peer reviewer provides little to no actionable feedback. 2) The “Technical Nitpicker”: This peer reviewer focuses on small Python-specific issues, but fails to communicate constructively. 3) The “Nit” Commenter: This peer reviewer prefaces every comment with “nit,” while offering unclear, yet technically valid suggestions

Using these archetypes, we will explore Python-specific technical topics (such as pass by reference vs. pass by value), while delving into how to communicate and deliver feedback in a clear and actionable manner. Using real-world examples, attendees will learn how to: a) Identify and address technical issues in Python PRs b) Communicate feedback effectively c) Balance technical rigor with constructive feedback d) Communicate their peer review comments clearly