As insurers increasingly leverage IoT data to personalize policy pricing, reconciling disparate datasets across devices, policies, and insurers becomes mission-critical. In this session, learn how Nationwide transitioned from prototype workflows in Dataiku to a hardened data stack on Databricks, enabling scalable data governance and high-impact analytics. Discover how the team orchestrates data reconciliation across Postgres, Oracle, and Databricks to align customer driving behavior with insurer and policy data—ensuring more accurate, fair discounts for policyholders. With Anomalo’s automated monitoring layered on top, Nationwide ensures data quality at scale while empowering business units to define custom logic for proactive stewardship. We’ll also look ahead to how these foundations are preparing the enterprise for unstructured data and GenAI initiatives.

In today’s digital economy, real-time insights and rapid responsiveness are paramount to delivering exceptional user experiences and lowering TCO. In this session, discover a pioneering approach that leverages a low-latency streaming ETL pipeline built with Spark Structured Streaming and Databricks’ new OLTP-DB—a serverless, managed Postgres offering designed for transactional workloads. Validated in a live customer scenario, this architecture achieves sub-2 second end-to-end latency by seamlessly ingesting streaming data from Kinesis and merging it into OLTP-DB. This breakthrough not only enhances performance and scalability but also provides a replicable blueprint for transforming data pipelines across various verticals. Join us as we delve into the advanced optimization techniques and best practices that underpin this innovation, demonstrating how Databricks’ next-generation solutions can revolutionize real-time data processing and unlock a myriad of new use cases in data landscape.

Unity Catalog puts variety of schemas into a centralized repository, now the developer community wants more productivity and automation for schema inference, translation, evolution and optimization especially for the scenarios of ingestion and reverse-ETL with more code generations.Coinbase Data Platform attempts to pave a path with "Schemaster" to interact with data catalog with the (proposed) metadata model to make schema translation and evolution more manageable across some of the popular systems, such as Delta, Iceberg, Snowflake, Kafka, MongoDB, DynamoDB, Postgres...This Lighting Talk covers 4 areas: The complexity and caveats of schema differences among The proposed field-level metadata model, and 2 translation patterns: point-to-point vs hub-and-spoke Why Data Profiling be augmented to enhance schema understanding and translation Integrate it with Ingestion & Reverse-ETL in a Databricks-oriented eco system Takeaway: standardize schema lineage & translation

Lakebase is a new Postgres-compatible OLTP database designed to support intelligent applications. Lakebase eliminates custom ETL pipelines with built-in lakehouse table synchronization, supports sub-10ms latency for high-throughput workloads, and offers full Postgres compatibility, so you can build applications more quickly.In this session, you’ll learn how Lakebase enables faster development, production-level concurrency, and simpler operations for data engineers and application developers building modern, data-driven applications. We'll walk through key capabilities, example use cases, and how Lakebase simplifies infrastructure while unlocking new possibilities for AI and analytics.

Lakeflow Connect streamlines the ingestion of incremental data from popular databases like SQL Server and PostgreSQL. In this session, we’ll review best practices for networking, security, minimizing database load, monitoring and more — tailored to common industry scenarios. Join us to gain practical insights into Lakeflow Connect's functionality so that you’re ready to build your own pipelines. Whether you're looking to optimize data ingestion or enhance your database integrations, this session will provide you with a deep understanding of how Lakeflow Connect works with databases.

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.