How Wherobots Works
Wherobots enables users to easily develop, test, and deploy in-situ geospatial data stack in the cloud. Users do not have to worry about the hassle of cloud administration, workload scalability, and geospatial processing support / optimization.
Connect your Wherobots account to the cloud database where the data is stored using our SaaS web interface
Develop your geospatial data science, machine learning or analytics application using Sedona Developer Tool
Schedule automatic deployment of your geospatial pipeline to the cloud data platform and monitor the performance in Wherobots
Consume the outcome of your geospatial analytics task. Consumption model can be through a single geospatial map visualization or API calls
Geospatial Data Science Acceleration
Hassle-free tool to develop, debug, and test geospatial data science, machine learning and analytics applications. Develop your geospatial data stack using Spatial Python and/or Spatial SQL using Apache Sedna.
Cloud-Integrated Geospatial pipeline
Deploy your developed geospatial data program with major cloud data services such as Databricks, Snowflake, AWS, Azure, and Google Cloud. The deployment process can be automatically scheduled by the user. Users do not have to worry about the hassle of cloud administration, workload scalability, and geospatial processing support / optimization.
Geospatial Data Stack Versatility
Developers can also integrate their code with popular data tools such as Spark, Flink, Hadoop, or PostgreSQL. Wherobots provides a GeoPandas adaptor to integrate Apache Sedona with GeoPandas shapely and Geodataframe. That feature allows python users to keep utilizing their beloved GeoPandas while using Apache Sedona for the heavy lifting geospatial data processing.
Fully Managed Spatial Data Pipeline
Run your geospatial data analytics application on massive data with fully managed in-situ spatial data partitioning, indexing, and tuning. Wherobots also provides at the developer's disposal out-of-the-box implementation of parallelized geospatial query processing algorithms.