🐘 The spatial SQL ecosystem has never been so full, and more and more tools are supporting spatial SQL from modern analytical databases and data warehouses to more traditional database systems. Here are all the tools supporting spatial SQL today. (link in comments)
🌐 PostGIS - Open source, extensive functions
📦 SpatiaLite - Lightweight, SQLite spatial extension
💾 MySQL - Common, Oracle-maintained, relational
🌟 Oracle Database - Proprietary, enterprise-grade spatial support
🏢 Microsoft SQL Server - Comprehensive, enterprise spatial database
🔍 Informix Spatial DataBlade - IBM-owned, early spatial database
🖥️ IBM Db2 - Robust, enterprise spatial features
📚 Databricks - Spark-based analytics platform
☁️ AWS Redshift - Cloud-based, large-scale data warehouse
📊 GCP BigQuery - Scalable, cloud data warehouse
❄️ Snowflake - Cloud data warehouse, native geometry
⚡ Apache Spark - Big data processing leader
🔗 Apache Sedona (GeoSpark) - Extends Spark, supports geometry
🦆 DuckDB - No-dependency, columnar-vector execution
📍 Apache Pinot - Real-time analytics, SQL support
🌀 Presto - Distributed SQL query engine
🎯 Trino - Presto fork, parallel processing
Let me know which tools you are excited about and want to use this coming year, and what types of resources would be helpful in learning these tools!
#gis #moderngis #geospatial #spatialsql #postgis #bigquery #snowflake #redshift #duckdb #trino #prestodb #databricks #sql #dataengineering #spatialanalytics