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Welcome to This Month In Wherobots the monthly developer newsletter for the Wherobots & Apache Sedona community! In this edition we cover SedonaSnow: Apache Sedona on Snowflake, accelerating your GIS pipeline with Apache Sedona, exploring Global Fishing Watch public data with SedonaDB and GeoParquet, and a look at new features and updates in the 1.5.1 release of Apache Sedona.
Apache Sedona, the scalable open-source geospatial compute engine is now available on Snowflake via the Snowflake Marketplace or via manual installation. The SedonaSnow plugin brings Apache Sedona’s Spatial SQL functionality to Snowflake via 130+ Sedona SQL “ST” SQL functions that can be used alongside Snowflake SQL.
Read More About Using SedonaSnow In Snowflake In This Tutorial
This month’s Wherobots & Apache Sedona featured community members are Alihan Zihna, Lead Data Scientist at CKDelta and Fernando Palacios, Director of Data Science & Data Engineering also at CKDelta. Alihan and Fernando presented “GIS Pipeline Acceleration With Apache Sedona” at the Data + AI Summit where they share how they were able to improve the performance and innovation of their geospatial analysis pipelines, going from a pipeline that took 48 hours to complete down to 10 minutes using Apache Sedona. Thank you Fernando and Alihan for being a part of the community and sharing your work!
In this presentation from Data + AI Summit, Fernando and Alihan discuss some of the various usecases for working with large-scale geospatial data at conglomerate CKDelta, part of the Hutchinson Group which operates ports, utility networks, retail stores and mobile telecom networks with hundreds of millions of users across dozens of countries. They discuss how geospatial analytics at scale is important for identifying water leakage in their utility network, understanding customer satisfaction, identifying sites for electric vehicle charging station installation, and forecasting the supply and demand of energy. They provide a technical overview of Apache Sedona and share the results of improving and extending their geospatial analytics pipelines including one process that reduced running time from 48 hours to 10 minutes using Apache Sedona.
Watch the recording of “GIS Pipeline Acceleration With Apache Sedona”
In Part 2 of our Getting Started With Wherobots Cloud & SedonaDB series we dive into the Wherobots Notebook Environment including how to configure and start notebook runtimes, an overview of the sample notebooks included in Wherobots Cloud, and how to use version control like git with notebooks. If you missed it check out Part 1: An Overview of Wherobots Cloud or sign up for a free Wherobots Cloud account to get started directly.
Read More About The Wherobots Notebooks Environment
This post is a hands-on look at offshore ocean infrastructure and industrial vessel activity with SedonaDB using data from Global Fishing Watch. We also see how GeoParquet can be used with this data to improve the efficiency of data retrieval and enable large-scale geospatial visualization using GeoArrow and the Lonboard Python visualization library.
Read “Exploring Global Fishing Watch Public Data With SedonaDB & GeoParquet”
The most recent release of Apache Sedona introduces some exciting new updates including support for Spark 3.5, 20+ new raster functions, 7 new vector functions, support for running Sedona in Snowflake with SedonaSnow, updates to Sedona’s GeoParquet reader and writer, and more! The updated raster functions include RS_ZonalStats for computing zonal statistics, RS_Tile and RS_TileExplode to enable tiling large rasters, and updates to RS_MapAlgebra to enable user defined raster functions that can work across multiple rasters. Updated vector functions include ST_IsValidReason which exposes the reason geometries might not be valid, and ST_LineLocatePoint which can be useful for map matching and snapping data to road networks.
RS_ZonalStats
RS_Tile
RS_TileExplode
RS_MapAlgebra
ST_IsValidReason
ST_LineLocatePoint
Read More About Apache Sedona 1.5.1 In The Release Notes
Each month you can find a new livestream tutorial on the Wherobots YouTube channel. January’s livestream was all about working with GeoParquet and Havasu tables in SedonaDB. We dig in to understanding some of the optimizations built into the Apache Parquet format to learn how Parquet delivers efficient data storage and data retrieval before exploring the GeoParquet specification for storing geospatial data in Parquet. We cover loading, analyzing, and creating GeoParquet files using SedonaDB with a focus on comparing performance of various GeoParquet partitioning strategies. Finally, we see how the Havasu extension to the Apache Iceberg table format enables working with both vector and raster geospatial data backed by GeoParquet but with the familiar developer experience of SQL tables.
Watch The Recording: Hands-On With Havasu And GeoParquet
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