​​Bay Area Apache Polaris™ (incubating) Meetup!

​Join us on November 19th (Wednesday) from 5:30-8:30 PM at the Snowflake Menlo Park Office.

​​​Connect with fellow community members, share insights, and dive into the latest developments in the world of Apache Polaris!

​​Note on Parking: There is free parking available at the event.

​​Agenda

​​​5:30 PM - 6:30 PM: Doors Open & Networking

​​​6:30 PM - 8:00 PM: Welcome Remarks & Presentations!

​​​8:00 PM - 8:30 PM: More Networking

​​The event will focus on use cases of and new developments in Apache Polaris (https://polaris.apache.org/)

​Sessions

​Apache Polaris: From Iceberg Catalog to the Universal Lakehouse Catalog, Alex Merced, Dremio

​The evolution of Apache Polaris represents a pivotal step toward unifying data across engines, formats, and clouds. Originally designed as an open standard catalog for Apache Iceberg tables, Polaris is rapidly expanding its scope to become the universal catalog for the modern lakehouse. This talk explores the vision, architecture, and milestones on Polaris’s journey—from governing Iceberg tables with fine-grained access control to enabling federated metadata, credential vending, and interoperability across diverse storage and compute environments. Attendees will learn how Polaris is shaping the foundation for an open, standardized, and truly universal lakehouse ecosystem.

​Introducing Generic Tables in Polaris: Expanding Beyond Iceberg, Yun Zou and Yufei Gui, Snowflake

​With Apache Polaris 1.0, users gained a powerful catalog for managing Apache Iceberg tables through the Iceberg REST Catalog API. But modern data ecosystems are diverse — many users and analytics engines like Apache Spark, Trino, and Snowflake also rely on other table formats such as Delta.

​To meet this need for greater flexibility and interoperability, Polaris 1.1 introduces Generic Tables — a major step forward in expanding Polaris beyond Iceberg. This new capability lays the foundation for managing multiple table formats within a unified catalog. Alongside this release, we're also introducing a Spark Catalog plugin, enabling a seamless end-to-end experience with Spark for non-Iceberg tables.

​Build a real OPEN LakeHouse with StarRocks and Polaris, Alvin Zhao, CelerData

​Enterprises want lakehouse speed without vendor lock-in. This talk demonstrates how to build a truly open lakehouse by combining StarRocks as the high-performance SQL engine with Apache Polaris as the open catalog—on top of open table formats, such as Apache Iceberg. I’ll start with a quick primer on what StarRocks is (vectorized execution, cost-based optimizer, lake-native materialized views). We’ll examine the missing piece in many “open” deployments—the catalog—and how an open, spec-driven catalog completes the promise of an open table format. Next, I’ll walk through how StarRocks integrates with Polaris today. Finally, I’ll outline our roadmap to align StarRocks RBAC with Polaris authorization for consistent, central governance across engines.

​AI Needs Context: Enabling Multi-Engine Workflows with Apache Iceberg and Polaris, Andrew Madson, Fivetran

​AI initiatives thrive on rich context. Agents pull together diverse data across the enterprise and perform varied tasks for multiple teams. No single query engine excels at everything. Some tasks demand massive distributed processing (e.g., preparing a large training dataset), while others benefit from lightweight local analysis or interactive SQL. Traditionally, using multiple engines meant copying data into different silos or formats for each tool, adding cost and complexity. Apache Iceberg and Polaris now offer a better approach. Together, these open technologies enable multi-engine AI workflows on a single source of truth, allowing teams to pick the best tool for each job without data duplication or vendor lock-in.

​​About Dremio

​​Dremio is the intelligent lakehouse platform that accelerates AI and analytics with AI-ready data products, unified access, and automated performance optimization. Built on Apache Iceberg, Arrow, and Polaris, Dremio combines a business-friendly semantic layer, a high-speed SQL engine, and an enterprise-grade catalog to deliver fast, governed, and discoverable data across cloud and on-prem environment

​​​​📚 Learn more about Dremio

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​About Snowflake

​​​Snowflake makes enterprise AI easy, efficient and trusted. More than 10,000 companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. Snowflake provides native support for Apache Iceberg™ and Apache Polaris™ (incubating).

​​​📚 Check out how Snowflake can power your open data lakehouse

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​About Fivetran

​Fivetran is the global leader in modern data integration. Our mission is to make access to data as simple and reliable as electricity. Built for the cloud, Fivetran enables data teams to effortlessly centralize and transform data from hundreds of SaaS and on-prem data sources into high-performance cloud destinations. Fast-moving startups to the world’s largest companies use Fivetran to accelerate modern analytics and operational efficiency, fueling data-driven business growth. Fivetran is headquartered in Oakland, California, with offices around the world.