Seattle Apache Iceberg™ Community Meetup
Seattle Apache Iceberg™ Community Meetup! 🧊❄️🍦
Join us on May 6th (Tuesday) from 4:30-7:30 PM
At Databricks Bellevue Office
Connect with fellow enthusiasts, share insights, and dive into the latest developments in the Apache Iceberg™ ecosystem! Whether you're a seasoned pro or new to Apache Iceberg, this meetup is the perfect place to exchange ideas and spark innovation.
Agenda
4:30p - 5:30p: Doors Open & Networking 💃
5:30p - 6:30p: Welcome Remarks & Presentations!
6:30p - 7:30p: More Networking 🕺
The event will focus on innovations in Apache Iceberg (https://iceberg.apache.org/)
We will discuss topics around Open-Source Data Analytics, Open Table Formats (OTF), software concepts like Transactional Data Lakes or Lakehouse, advancements in AI/ML including generative AI, and many more topics of mutual interest that leverage Apache Iceberg.
During the sessions, we will provide you tips to get involved within the community, you will learn more about how the community is collaborating to grow the technology, and software/solutions that ease problem solving and improve user experiences.
Topic 1: Multi-File Format for Apache Iceberg - Using Iceberg and Lance to Accelerate Your ML/AI Workloads
Abstract
This talk discusses the ongoing DataFile API project in Apache Iceberg, with technical details of the ongoing PRs and the prototype integration with the Lance file format. This talk will also cover how Iceberg and Lance combined together can speed up the ML feature engineering process, as well as the query execution process for specific AI workloads.
Speaker bio
Jack Ye (Software Engineer @ LanceDB)
Jack is an open source software engineer, currently working on projects including Apache Iceberg, Apache Polaris, Lance, Apache Spark, Trino, Olympia, etc.
Topic 2: Iceberg in the browser
Abstract
Wouldn't it be nice if you could view Iceberg tables directly in the browser, without dependency on spark, or in fact any serverside infrastructure at all?
I am the author of Icebird, a new open-source implementation of Iceberg written natively in JavaScript. In this talk I would share my journey of first writing a new dependency-free javascript parquet implementation: hyparquet. And then how I used hyparquet to write an Iceberg table reader for the browser.
Additionally I would answer the question: Why you would you WANT to move parquet and iceberg clients into the browser? Because it enables building entirely new kinds of simpler and more interactive data applications.
Speaker bio
Kenny Daniel (Founder @ Hyperparam)
Kenny Daniel is previously co-founder of Algorithmia and currently founder of Hyperparam, where he’s building tools for inspecting and curating massive AI datasets. With a background in AI research from CMU and USC, he’s focused on making machine learning infrastructure more practical, scalable, and data-centric.
Topic 3: Iceberg with Spice: AI Agents at the Edge
Abstract
It will demonstrate how Spice.ai uses Iceberg to support AI at the edge, with a live demo of real-time decision-making. Aimed at data engineers and developers, it aligns with the meetup’s focus on lakehouse architectures.
Speaker bio
Luke Kim (Founder @ Spice AI)
Luke Kim is the Founder and CEO of Spice AI, a Seattle-based, VC-backed startup building data and AI infrastructure for intelligent applications and agents.
Call for Proposals is Closed (Due 4/30)
Call for Proposals link
About CelerData
CelerData (powered by StarRocks) is the fastest query engine for customer-facing and AI-driven analytics at petabyte scale. With native Apache Iceberg integration, it delivers low-latency, high-concurrency queries directly on open data—without ingestion delays or costly pipelines.
Trusted by industry leaders like Pinterest, Tencent, and Expedia, CelerData powers the next generation of analytics on the Lakehouse.
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About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Condé Nast, Rivian, Shell and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI. Databricks is headquartered in San Francisco, with offices around the globe, and was founded by the original creators of Lakehouse and Apache Spark™.
📚 Check out Tabular has joined Databricks towards a joint vision of the open lakehouse.
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About AWS
Whether you're looking for generative AI, compute power, database storage, content delivery, or other functionality, AWS has the services to help you build sophisticated applications with increased flexibility, scalability, and reliability. AWS is the world's largest Cloud Services provider. https://aws.amazon.com/
At AWS, Apache Iceberg is an open-source table format that simplifies table management while improving performance. AWS analytics services such as Amazon SageMaker Lakehouse, Amazon S3Tables, Amazon EMR, Amazon Glue, Amazon Athena, and Amazon Redshift include native support for Apache Iceberg, so you can easily build transactional data lakes on top of Amazon Simple Storage Service (Amazon S3) on AWS.
Additional Resources and Information:
📚 Workshop: Running Apache Iceberg on AWS
📚 Blogs: Apache Iceberg on AWS
📚 AWS Prescriptive Guidance: Using Apache Iceberg on AWS
🖥️ Subscribe to AWS Events and AWS Developers
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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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