In this session, we will explore how Genie, an AI-driven platform transformed HVAC operational insights by leveraging Databricks offerings like Apache Spark, Delta Lake and the Databricks Data Intelligence Platform.Key contributions: Real-time data processing: Lakeflow Declarative Pipelines and Apache Spark™ for efficient data ingestion and real-time analysis. Workflow orchestration: Databricks Data Intelligence Platform to orchestrate complex workflows and integrate various data sources and analytical tools. Field Data Integration: Incorporating real-time field data into design and algorithm development, enabling engineers to make informed adjustments and optimize performance. By analyzing real-time data from HVAC installations, Genie identified discrepancies between design specs and field performance, allowing engineers to optimize algorithms, reduce inefficiencies and improve customer satisfaction. Discover how Genie revolutionized HVAC management and apply to your projects.

This course provides a comprehensive review of DevOps principles and their application to Databricks projects. It begins with an overview of core DevOps, DataOps, continuous integration (CI), continuous deployment (CD), and testing, and explores how these principles can be applied to data engineering pipelines. The course then focuses on continuous deployment within the CI/CD process, examining tools like the Databricks REST API, SDK, and CLI for project deployment. You will learn about Databricks Asset Bundles (DABs) and how they fit into the CI/CD process. You’ll dive into their key components, folder structure, and how they streamline deployment across various target environments in Databricks. You will also learn how to add variables, modify, validate, deploy, and execute Databricks Asset Bundles for multiple environments with different configurations using the Databricks CLI. Finally, the course introduces Visual Studio Code as an Interactive Development Environment (IDE) for building, testing, and deploying Databricks Asset Bundles locally, optimizing your development process. The course concludes with an introduction to automating deployment pipelines using GitHub Actions to enhance the CI/CD workflow with Databricks Asset Bundles. By the end of this course, you will be equipped to automate Databricks project deployments with Databricks Asset Bundles, improving efficiency through DevOps practices. Pre-requisites: Strong knowledge of the Databricks platform, including experience with Databricks Workspaces, Apache Spark, Delta Lake, the Medallion Architecture, Unity Catalog, Delta Live Tables, and Workflows. In particular, knowledge of leveraging Expectations with Lakeflow Declarative Pipelines. Labs : Yes Certification Path: Databricks Certified Data Engineer Professional

Quantum Capital Group (QCG) screens hundreds of deals across the global Sustainable Energy Ecosystem, requiring deep technical due diligence. With over 1.5 billion records sourced from public, premium and proprietary datasets, their challenge was how to efficiently curate, analyze and share this data to drive smarter investment decisions. QCG partnered with Databricks & Tiger Analytics to modernize its data landscape. Using Delta tables, Spark SQL, and Unity Catalog, the team built a golden dataset that powers proprietary evaluation models and automates complex workflows. Data is now seamlessly curated, enriched and distributed — both internally and to external stakeholders — in a secure, governed and scalable way. This session explores how QCG’s investment in data intelligence has turned an overwhelming volume of information into a competitive advantage, transforming deal evaluation into a faster, more strategic process.

Dynamic policy enforcement is increasingly critical in today's landscape, where data compliance is a top priorities for companies, individuals, and regulators alike. In this talk, Walaa explores how LinkedIn has implemented a robust dynamic policy enforcement engine, ViewShift, and integrated it within its data lake. He will demystify LinkedIn's query engine stack by demonstrating how catalogs can automatically route table resolutions to compliance-enforcing SQL views. These SQL views possess several noteworthy properties: Auto-Generated: Created automatically from declarative data annotations. User-Centric: They honor user-level consent and preferences. Context-Aware: They apply different transformations tailored to specific use cases. Portable: Despite the SQL logic being implemented in a single dialect, it remains accessible across all engines. Join this session to learn how ViewShift helps ensure that compliance is seamlessly integrated into data processing workflows.

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.

As cybersecurity threats grow in volume and complexity, organizations must efficiently process security telemetry for best-in-class detection and mitigation. Barracuda’s XDR platform is redefining security operations by layering advanced detection methodologies over a broad range of supported technologies. Our vision is to deliver unparalleled protection through automation, machine learning and scalable detection frameworks, ensuring threats are identified and mitigated quickly. To achieve this, we have adopted Databricks as the foundation of our security analytics platform, providing greater control and flexibility while decoupling from traditional SIEM tools. By leveraging Lakeflow Declarative Pipelines, Spark Structured Streaming and detection-as-code CI/CD pipelines, we have built a real-time detection engine that enhances scalability, accuracy and cost efficiency. This session explores how Databricks is shaping the future of XDR through real-time analytics and cloud-native security.

XGBoost is one of the off-the-shelf gradient boosting algorithms for analyzing tabular datasets. Unlike deep learning, gradient-boosting decision trees require the entire dataset to be in memory for efficient model training. To overcome the limitation, XGBoost features a distributed out-of-core implementation that fetches data in batch, which benefits significantly from the latest NVIDIA GPUs and the NVLink-C2C’s ultra bandwidth. In this talk, we will share our work on optimizing XGBoost using the Grace Blackwell super chip. The fast chip-to-chip link between the CPU and the GPU enables XGBoost to scale up without compromising performance. Our work has effectively increased XGBoost’s training capacity to over 1.2TB on a single node. The approach is scalable to GPU clusters using Spark, enabling XGBoost to handle terabytes of data efficiently. We will demonstrate combining XGBoost out-of-core algorithms with the latest connect ML from Spark 4.0 for large model training workflows.

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.

What if you could run Spark jobs without worrying about clusters, versions and upgrades? Did you know Spark has this functionality built-in today? Join us to take a look at this functionality — Spark Connect. Join us to dig into how Spark Connect works — abstracting away Spark clusters away in favor of the DataFrame API and unresolved logical plans. You will learn some of the cool things Spark Connect unlocks, including: Moving you from thinking about clusters to just thinking about jobs Making Spark code more portable and platform agnostic Enabling support for languages such as Go

In today's data-driven world, the ability to efficiently manage and transform data is crucial for any organization. This presentation will explore the process of converting a complex and messy workflow into a clean and simple Lakeflow Declarative Pipelines at a large integrated health system, Intermountain Health.Alteryx is a powerful tool for data preparation and blending, but as workflows grow in complexity, they can become difficult to manage and maintain. Lakeflow Declarative Pipelines, on the other hand, offers a more democratized, streamlined and scalable approach to data engineering, leveraging the power of Apache Spark and Delta Lake.We will begin by examining a typical legacy workflow, identifying common pain points such as tangled logic, performance bottlenecks and maintenance challenges. Next, we will demonstrate how to translate this workflow into a Lakeflow Declarative Pipelines, highlighting key steps such as data transformation, validation and delivery.

We’ll explore how CipherOwl Inc. constructed a near real-time, multi-chain data lakehouse to power anti-money laundering (AML) monitoring at a petabyte scale. We will walk through the end-to-end architecture, which integrates cutting-edge open-source technologies and AI-driven analytics to handle massive on-chain data volumes seamlessly. Off-chain intelligence complements this to meet rigorous AML requirements. At the core of our solution is ChainStorage, an OSS started by Coinbase that provides robust blockchain data ingestion and block-level serving. We enhanced it with Apache Spark™ and Arrow™, coupled for high-throughput processing and efficient data serialization, backed by Delta Lake and Kafka. For the serving layer, we employ StarRocks to deliver lightning-fast SQL analytics over vast datasets. Finally, our system incorporates machine learning and AI agents for continuous data curation and near real-time insights, which are crucial for tackling on-chain AML challenges.

Table formats like Delta Lake and Iceberg have been game changers for pushing lakehouse architecture into modern Enterprises. The acquisition of Tabular added Iceberg to the Databricks ecosystem, an open format that was already well supported by processing engines across the industry. At HelloFresh we are building a lakehouse architecture that integrates many touchpoints and technologies all across the organization. As such we chose Iceberg as the table format to bridge the gaps in our decentralized managed tech landscape. We are leveraging Unity Catalog as the Iceberg REST catalog of choice for storing metadata and managing tables. In this talk we will outline our architectural setup between Databricks, Spark, Flink and Snowflake and will explain the native Unity Iceberg REST catalog, as well as catalog federation towards connected engines. We will highlight the impact on our business and discuss the advantages and lessons learned from our early adopter experience.

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.Is stream processing the future? We think so — and we’re building it with you using the latest capabilities in Apache Spark™ Structured Streaming. If you're a power user, this session is for you: we’ll demo new advanced features, from state transformations to real-time mode. If you prefer simplicity, this session is also for you: we’ll show how Lakeflow Declarative Pipelines simplifies managing streaming pipelines. And if you’re somewhere in between, we’ve got you covered — we’ll explain when to use your own streaming jobs versus Lakeflow Declarative Pipelines.

In the dynamic world of sports betting, precision and adaptability are key. Sports traders must navigate risk management, limitations of data feeds, and much more to prevent small model miscalculations from causing significant losses. To ensure accurate real-time pricing of hundreds of interdependent markets, traders provide key inputs such as player skill-level adjustments, whilst maintaining precise correlations. Black-box models aren’t enough— constant feedback loops drive informed, accurate decisions. Join DraftKings as we showcase how we expose real-time metrics from our simulation engine, to empower traders with deeper insights into how their inputs shape the model. Using Spark Structured Streaming, Kafka, and Databricks dashboards, we transform raw simulation outputs into actionable data. This transparency into our engines enables fine-grained control over pricing― leading to more accurate odds, a more efficient sportsbook, and an elevated customer experience.

Delta Lake has proven to be an excellent storage format. Coupled with the Databricks platform, the storage format has shined as a component of a distributed system on the lakehouse. The pairing of Delta and Spark provides an excellent platform, but users often struggle to perform comparable work outside of the Spark ecosystem. Tools such as delta-rs, Polars and DuckDb have brought access to users outside of Spark, but they are only building blocks of a larger system. In this 40-minute talk we will demonstrate how users can use data products on the Nextdata OS data mesh to interact with the Databricks platform to drive Delta Lake workflows. Additionally, we will show how users can build autonomous data products that interact with their Delta tables both inside and outside of the lakehouse platform. Attendees will learn how to integrate the Nextdata OS data mesh with the Databricks platform as both an external and integral component.

Grace-Blackwell is NVIDIA’s most recent GPU system architecture. It addresses a key concern of query engines: fast data access. In this session, we will take a close look at how GPUs can accelerate data analytics by tracing how a row flows through a GPU-enabled query engine.Query engines read large data from CPU memory or from disk. On Blackwell GPUs, a query engine can rely on hardware-accelerated decompression of compact formats. The Grace-Blackwell system takes data access performance even further, by reading data at up to 450 GB/s across its CPU to GPU interconnect. We demonstrate full end-to-end SQL query acceleration using GPUs in a prototype query engine using industry standard benchmark queries. We compare the results to existing CPU solutions.Using Apache Spark™ and the RAPIDS Accelerator for Apache Spark, we demonstrate the impact GPU acceleration has on the performance of SQL queries at the 100TB scale using NDS, a suite that simulates real-world business scenarios.

Don’t you just hate telling your customers “No”? “No, I can’t get you the data that quickly”, or “No that logic isn’t possible to implement” really aren’t fun to say. But what if you had a tool that would allow you to implement those use cases? What if it was in a technology you were already familiar with — say, Spark Structured Streaming? There is a brand new arbitrary stateful operations API called TransformWithState, and after attending this deep dive you won’t have to say “No” anymore. During this presentation we’ll go through some real-world use cases and build them step-by-step. Everything from state variables, process vs. event time, watermarks, timers, state TTL, and even how you can initialize state with the checkpoint of another stream. Unlock your use cases with the power of Structured Streaming’s TransformWithState!

Spark Connect, first included for SQL/DataFrame API in Apache Spark 3.4 and recently extended to MLlib in 4.0, introduced a new way to run Spark applications over a gRPC protocol. This has many benefits, including easier adoption for non-JVM clients, version independence from applications and increased stability and security of the associated Spark clusters. The recent Spark Connect extension for ML also included a plugin interface to configure enhanced server-side implementations of the MLlib algorithms when launching the server. In this talk, we shall demonstrate how this new interface, together with Spark SQL’s existing plugin interface, can be used with NVIDIA GPU-accelerated plugins for ML and SQL to enable no-code change, end-to-end GPU acceleration of Spark ETL and ML applications over Spark Connect, with optimal performance up to 9x at 80% cost reduction compared to CPU baselines.

Swiggy, India's leading quick commerce platform, serves ~13 million users across 653 cities, with 196,000 restaurant partners and 17,000 SKUs. To handle this scale, Swiggy developed a secure, scalable AI platform processing millions of predictions per second. The tech stack includes Apache Kafka for real-time streaming, Apache Spark on Databricks for analytics and ML, and Apache Flink for stream processing. The Lakehouse architecture on Delta ensures data reliability, while Unity Catalog enables centralized access control and auditing. These technologies power critical AI applications like demand forecasting, route optimization, personalized recommendations, predictive delivery SLAs, and generative AI use cases.Key Takeaway:This session explores building a data platform at scale, focusing on cost efficiency, simplicity, and speed, empowering Swiggy to seamlessly support millions of users and AI use cases.