Apache Polaris™ Meetup Seattle
Seattle Apache Polaris™ Meetup!
Join us on February 26th (Thursday) from 5:30-9:00 PM at Microsoft.
Location: Microsoft Redmond, Building 7 Room 1.2B-Cypress (7/1.2B-Cypress).
Free parking on campus! Park at Microsoft Building 5 (around the corner)
Connect with fellow community members, share insights, and dive into the latest developments in the world of Apache Polaris!
Agenda
5:30 PM - 6:15 PM: Doors Open & Networking
6:15 PM - 8:15 PM: Welcome Remarks & Presentations!
8:15 PM - 9:00 PM: More Networking
The event will focus on use cases of and new developments in Apache Polaris (https://polaris.apache.org/)
Sessions
Want to speak? Submit here.
Mapping Legacy and Heterogeneous Datalakes in Apache Polaris, Maninder Parmar @ Snowflake
As the ecosystem standardizes on the Iceberg REST catalog protocol, organizations face a practical reality: vast amounts of data still live in legacy Hive metastores, heterogeneous lake formats, and engine-specific pipelines. Full migrations are expensive, risky, and operationally disruptive.
This talk explores how Apache Polaris’s read-only External Catalog enables a zero-copy metadata bridge across legacy and heterogeneous data lakes. Instead of crawling or polling remote systems, Polaris adopts a push-based, stateless architecture where producer-side sync agents notify the catalog via the Notification API. This eliminates credential sprawl, avoids long-lived cross-account access, and maintains strict unidirectional trust boundaries.
Integrating Apache Polaris with PuppyGraph for Real-Time Graph Analysis, Jaz Ku @ PuppyGraph
Teams that have cataloged their Iceberg tables in Apache Polaris have already done the hard part. Access is locked down, and multiple engines can work from the same governed data.
But when relationship questions come up, like fraud rings, dependency chains, “who touched what,” or multi-hop paths, the usual approach is to copy data into a graph database. That often means rebuilding pipelines and redoing governance from scratch.
In this talk, we’ll show a different approach. Run graph traversals directly on existing Iceberg tables through a Polaris catalog, with no data duplication and no separate graph store to maintain. We’ll walk through a simple deploy → connect → query flow and share examples where graph queries really shine.
About Snowflake
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About Microsoft Fabric
As AI reshapes every industry, one truth remains constant: data is no longer just an asset—it’s your competitive edge. The pace of AI demands easy data access, faster insights, and the ability to iterate without friction. Yet many organizations are held back by fragmented data estates and legacy systems. Microsoft Fabric was designed to meet this moment—to unify your data, simplify your architecture, and accelerate your path to becoming an AI-led organization.
Apache Iceberg is the connective tissue enabling native and 3rd parties to integrate and interoperate in a seamless manner without forcing users to build or maintain complex lake management solutions. Fabric OneLake is a single place to store and access data in Iceberg and Delta Lake formats. Additionally, it provides a unified catalog powered by Iceberg REST specification enabling seamless integration with your favorite tools like Fabric native engines, Databricks, Snowflake, ClickHouse and many more.
Introducing Microsoft Fabric
Introducing Microsoft OneLake
Learn more about Iceberg in Microsoft Fabric
Connect to OneLake using Iceberg REST-powered Table APIs