Raster Data Analysis, Processing Petabytes of Agronomic Data, Overview of Sedona 1.5, and Unlocking The Spatial Frontier – This Month In Wherobots

Welcome to This Month In Wherobots, the monthly newsletter for data practitioners in the Apache Sedona and Wherobots community. This month we’re exploring raster data analysis with Spatial SQL, processing petabytes of agronomic data with Apache Sedona, a deep dive on new features added in the 1.5 release series, and an overview of working with files in Wherobots Cloud.

Raster Data Analysis With Spatial SQL & Apache Sedona

One of the strengths of Apache Sedona and Wherobots Cloud is the ability to work with large scale vector and raster geospatial data together using Spatial SQL. This post (and video) takes a look at how to get started working with raster data in Sedona using Spatial SQL and some of the use cases for raster data analysis including vector / raster join operations, zonal statistics, and using raster map algebra.

Read The Article: Raster Data Analysis With Spatial SQL & Apache Sedona

Featured Community Member: Luiz Santana

This month’s Wherobots & Apache Sedona featured community member is Luiz Santana. Luiz is Co-Founder and CTO of Leaf Agriculture. He has extensive experience as a former data architect and developer. Luiz has a PhD in Computer Science from Universidade Federal de Santa Catarina and spent time researching data processing and integration in highly scalable environments. Leaf Agriculture is building the unified API for food and agriculture by leveraging large-scale agronomic data. Luiz has given several presentations at conferences such as Apache Sedona: How To Process Petabytes of agrnomic data with Spark, and Perspectives on the use of data in Agriculture, which covers how Leaf uses Apache Sedona to analyze large-scale agricultural data and how Sedona fits into their stack alongside other technologies. Thank you Luiz for your work with the Apache Sedona and Wherobots community and sharing your knowledge and experience!

Apache Sedona: How To Process Petabytes of Agronomic Data With Spark

In this presentation from The Developer’s Conference Luiz Santana shares the experience of using Apache Sedona at Leaf Agriculture to process petabytes of agronomic data from satellites, agricultural machines, drones and other sensors. He discusses how Leaf uses Sedona for tasks such as geometry intersections, geographic searches, and polygon transformations with high performance and speed. Luiz also presented Perspectives On The Use of Data in Agriculture which covers some of the data challenges that Leaf handles and an overview of the technologies used to address these challenges, including Apache Sedona.

See The Slides From The Presentation

Working With Files – Getting Started With Wherobots Cloud

This post takes a look at loading and working with our own data in Wherobots Cloud as well as creating and saving data as the result of our analysis, such as the end result of a data pipeline. It covers importing files in various formats including CSV, GeoJSON, Shapefile, and GeoTIFF in Wherobots Cloud, working with AWS S3 cloud object storage, and creating GeoParquet files using Apache Sedona.

Read The Post: Working With Files – Getting Started With Wherobots Cloud

Introducing Sedona 1.5: Making Sedona the most comprehensive & scalable spatial data processing and ETL engine for both raster and vector data

The 1.5 series of Apache Sedona represents a leap forward in geospatial processing that adds essential features and enhancements to make Sedona a comprehensive, all-in-one cluster computing engine for geospatial vector and raster data analysis. This post covers XYZM coordinates and SRID, vector and raster joins, raster data manipulation, visualization with SedonaKepler and SedonaPyDeck, GeoParquet reading and writing, H3 hexagons, and new cluster compute engine support.

Read The Post: Introducing Sedona 1.5 – Making Sedona The Most Comprehensive & Scalable Spatial Data Processing and ETL Engine For Both Raster and Vector Data

Unlocking the Spatial Frontier: The Evolution and Potential of spatial technology in Apple Vision Pro and Augmented Reality Apps

Apple adopted the term “spatial computing” when announcing the Apple Vision Pro to describe its new augmented reality platform. This post from Wherobots CEO Mo Sarwat examines spatial computing in the context of augmented reality experiences to explore spatial object localization and presentation and the role of spatial query processing and spatial data analytics in Apple Vision Pro.

Read The Post: Unlocking The Spatial Frontier

Upcoming Events

• Apache Sedona Community Office Hour – (Online Zoom Call – April 9, 2024) – Join the Apache Sedona community on the second Tuesday of each month for updates on the state of Apache Sedona, presentation and demo of recent features, and provide your input into the roadmap, future plans, and contribution opportunities.
• Training Series – Large Scale Geospatial Analytics With Graphs And The PyData Ecosystem – (Online – March 26, 2024) – For our monthly livestream this month we’re partnering with our friends at Neo4j for a live virtual training series focused on geospatial analytics, the PyData ecosystem, and how graphs and Neo4j can be used alongside Apache Sedona and Wherobots Cloud to make sense of geospatial data. Register for free here.
• Subsurface Conference – Havasu: A Table Format For Spatial Attributes In A Data Lake Architecture (Online – May 2, 2024) – This talk at the Subsurface Live conference will introduce the Havasu open table format that extends Apache Iceberg to support spatial data.
• SW2 Conference – Cloud Native Geospatial Analytics With Apache Sedona, GeoParquet and Apache Iceberg (Denver – May 15, 2024) – This talk will cover recent developments in the cloud-native data ecosystem that address analyzing geospatial data at scale including Apache Sedona for executing large scale spatial queries, GeoParquet an optimized storage format for efficiently managing spatial data, and the Havasu extension to the Apache Iceberg open table format for managing tables in a spatial data lakehouse.