The Apache Iceberg™ community is introducing native geospatial type support, addressing key challenges in managing geospatial data at scale, including fragmented formats and inefficiencies in storing large spatial datasets. This talk will delve into the origins of the Iceberg geo type, its specification design and future goals. We will examine the impact on both the geospatial and Iceberg communities, in introducing a standard data warehouse storage layer to the geospatial community, and enabling optimized geospatial analytics for Iceberg users. We will also present a live demonstration of the Iceberg geo data type with Apache Sedona™ and Apache Spark™, showcasing how it simplifies and accelerates geospatial analytics workflows and queries. Finally, we will also provide an in-depth look at its current capabilities and outline the roadmap for future developments, and offer a perspective on its role in advancing geospatial data management in the industry.

Apache Iceberg is a popular table format for managing large analytical datasets. But replicating iceberg tables at scale can be a daunting task — especially when dealing with its hierarchical metadata. In this talk, we present an end-to-end workflow for replicating Apache Iceberg tables, leveraging Apache Spark to ensure that backup tables remain identical to their source counterparts. More excitingly, we have contributed these libraries back to the open-source community. Attendees will gain a comprehensive understanding of how to set up replication workflows for Iceberg tables, as well as practical guidance on how to manage and maintain replicated datasets at scale. This talk is ideal for data engineers, platform architects and practitioners looking to apply replication and disaster recovery for Apache Iceberg in complex data ecosystems.