FOUNDED BY THE ORIGINAL CREATORS OF APACHE SEDONA

2017

Jia Yu and Mo Sarwat released the first version of Sedona (back then named GeoSpark), which extended big data systems to support spatial data processing capabilities. In less than two years, the project gained traction and saw use in production use cases.

2020

Sedona entered the Apache Software Foundation. It provides support for both Apache Spark and Apache Flink as well as enables users to develop their spatial data processing jobs using Spatial SQL and Spatial Python.

2022

Jia and Mo saw modern analytics engines providing incomplete support for spatial data. In June of 2022, they founded Wherobots to bring first-class support for spatial data and compute in the cloud.

2023

Wherobots launched SedonaDB and Wherobots Cloud in October 2023, making Sedona’s spatial analytics technology available as a serverless cloud solution.

2024

Apache Sedona reaches over 40 million in downloads, 200% growth with over 2 million a month. The team raises its Series-A funding round and expands the product offering to include WherobotsAI for Raster Inference, integrations of WherobotsDB with the Spatial API and SDK. Delivers integration with AWS marketplace for easy adoption for Amazon customers.

2025

Wherobots led the standardization of spatial data in the Lakehouse by driving GEO support for Apache Iceberg and Parquet. The Apache Sedona community launched SedonaDB, and saw downloads soar past 62 million. The Wherobots team released WherobotsDB 2.0, achieving up to 7x query acceleration. New integrations with Databricks Unity Catalog and Amazon S3, alongside the launch of the Wherobots MCP server, established the foundation for AI-driven spatial intelligence.

2026

Looking forward, Wherobots plans to enable full AI orchestration of the physical world, allowing LLMs to execute complex data engineering workflows via the integration of the MCP server and RasterFlow. With RasterFlow now generally available, the platform expands its ecosystem with deeper developer tooling for VS Code and broader Spark environment support, making spatial data engineering fully accessible to AI agents.