Jan 2026 - Iceberg v3, Apache Polaris, and more!

## Apache Iceberg™ Meetup Atlanta

Join us on Wednesday, January 21, 2026, at the AWS Buckhead 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
​6:00 PM - 6:30 PM: Networking and Welcome Drinks
​​​6:30 PM - 7:45 PM: Welcome Remarks & Presentations!
​​​7:45 PM - 8:30 PM: Demos and Networking
​​
The event will focus on use cases around and 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.

## Talk 1: Apache Polaris - The Community Lakehouse Catalog

​Apache Polaris is a lakehouse catalog that is stewarded by a diverse community of contributors and vendors. Originally built as an open implementation of Apache Iceberg REST Catalog, Polaris has been expanding to become a lakehouse catalog for all data - structured, semi-structured, and unstructured. Let's talk about the future of this project!

Adam Christian is a software engineering leader who has been in the data analytics space for over a decade; building products and teams to democratize data. As a Principal Software Engineer at Dremio, he is focused on next-generation lakehouse catalogs including Arctic, Apache Polaris (incubating), and Dremio Open Catalog

## Talk 2: Understanding & Exploring Iceberg v3

Apache Iceberg is an open-source table format that provides database-like functionalities such as ACID transactions, schema evolution, and time travel for large analytic datasets stored in data lakes. Iceberg Spec v3 introduces significant advancements, including binary deletion vectors for faster deletes and updates, richer data types like variant for semi-structured data, nanosecond-precision timestamps, and built-in row lineage for enhanced data governance.

In addition to learning what these cool features can do for you, you'll see a live demo of many of the popular features and walk away with a hands-on exercise in case you want to learn by doing, too.

Lester Martin, Trino Developer Advocate at Starburst, is a seasoned developer advocate, trainer, blogger, and data engineer focused on data pipelines & data lake analytics using Trino, Iceberg, Hive, Spark, Flink, Kafka, NiFi, NoSQL databases, and, of course, classical RDBMSs. Lester is a polyglot programmer and comfortable in a variety of Dataframe APIs, including Spark, PyStarburst, Ibis, and pandas.

## Talk 3: Kastor: A Programmable Control Plane for Data Change Management with Apache Iceberg

Implementing Write-Audit-Publish (WAP) workflows at scale is a critical yet unsolved challenge in modern data engineering. While Apache Iceberg has become a foundational format for large-scale analytics, most organizations still rely on manual processes and brittle scripts to manage schema and data evolution—leaving systems vulnerable to downstream breakages, cost overruns, and compliance gaps.
Kastor introduces an intelligence layer and programmable control plane purpose-built for Apache Iceberg. It provides a native interface where automated agents can plan, review, and safely execute schema and data changes within existing Iceberg workflows. Each change is evaluated in real time for cost, performance impact, fragmentation, and downstream risk—enabling guarded automation with traceability, rollback, and human-in-the-loop approvals. The result is a self-auditing, self-documenting platform that accelerates iteration while maintaining trust and stability across the data lifecycle.

Jammond Ruffin is a data systems architect and founder of Kastor Data, where he builds scalable, secure, and intelligent data infrastructure for modern enterprises. Over the past 15 years, he has worked at the intersection of applied machine learning, data engineering, and platform architecture—delivering impactful solutions for organizations including Google/YouTube, and Reddit.