Managing spatial tables in Data Lakehouses with Iceberg
This post explains the benefits of the Lakehouse Architecture for spatial tables and how Lakehouses differ from data warehouses and data lakes.
The official source for Apache Sedona news, technical insights, release updates, and best practices in large-scale spatial data management.
Introducing SedonaDB: A single-node analytical database engine with geospatial as a first-class citizen
The Apache Sedona community is excited to announce the initial release of SedonaDB! 🎉
SedonaDB is the first open-source, single-node analytical database engine that treats spatial data as a first-class citizen. It is developed as a subproject of Apache Sedona.
Apache Sedona powers large-scale geospatial processing on distributed engines like Spark (SedonaSpark), Flink (SedonaFlink), and Snowflake (SedonaSnow). SedonaDB extends the Sedona ecosystem with a single-node engine optimized for small-to-medium data analytics, delivering the simplicity and speed that distributed systems often cannot.
Should You Use H3 for Geospatial Analytics? A Deep Dive with Apache Spark and Sedona
TL;DR The H3 spatial index provides a number of spatial functions and a consistent grid system for efficient data aggregation and visualization. H3 is an approximation that makes some computations run faster, but less accurately. Sedona supports H3 spatial index, but it's often preferable to use precise computations, especially when accuracy is important.
Welcome to the Apache Sedona Blog!
Welcome to the brand-new blog for Apache Sedona!
For several years, Apache Sedona has been the go-to open-source engine for processing massive geospatial datasets, extending Apache Spark to handle complex spatial operations with unparalleled speed and efficiency. Sedona's capabilities also extend beyond Spark, bringing spatial analytics to the Snowflake data warehouse with SedonaSnow and the real-time streaming engine Apache Flink with a Spatial SQL integration.