Jia Yu

The Python and R bindings for SedonaDB were purpose-built to accelerate performance-critical spatial analyses or scale existing analyses alongside existing free and open source tools.

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 Sedona has emerged as a pioneering cluster computing framework, setting new standards for processing vast quantities of spatial data. Building on its proven success in managing large-scale spatial vector datasets, this presentation shifts the focus towards Sedona's adeptness in raster data manipulation.

GeoParquet is a specification for storing geospatial data in Apache Parquet, bringing the advantages of the Parquet columnar data format to geospatial workloads. This talk will explore the GeoParquet specification and ecosystem with a focus on how Apache Sedona leverages GeoParquet for large-scale geospatial analytics.