Webinar: Why Modern Lakehouse Execution Layers Are Converging on Apache Arrow

​10 years of Arrow. 30 minutes to understand why it's everywhere.

​If you work with modern data infrastructure, Arrow is almost certainly running somewhere in your stack. Most engineers never notice it.

​Arrow solved a real problem: moving data between systems required serializing and deserializing at every boundary. CPU cycles, memory copies, latency. At scale, that cost compounds fast. Arrow's solution was a language-agnostic columnar memory format any system could share without copying. What started as a memory layout spec became the execution substrate of the modern data stack.

​In this 30-minute session, Badal Singh, who has contributed to Apache Iceberg Go and built OLake's Arrow-based ingestion writer at 550,000+ rows/second, will cover:

• ​From niche interoperability project to de-facto standard: Apache Arrow's 10-year journey

• ​What Arrow actually is beyond "columnar in-memory format" and why that definition undersells it

• ​How zero-copy data sharing eliminates serialization overhead and what that means for pipeline performance

• ​Where Arrow runs today: Spark, Pandas, ClickHouse, Polars, and inside open table formats like Apache Iceberg Go

• ​What's next: Arrow Flight, ADBC, nanoarrow, and the ecosystem reshaping how data systems talk to each other