![]() ![]() Geospatial workloads are typically complex and there is no one library fitting all use cases. Databricks UDAP delivers enterprise-grade security, support, reliability, and performance at scale for production workloads. It is powered by Apache Spark™, Delta Lake, and MLflow with a wide ecosystem of third-party and available library integrations. Scaling Geospatial Workloads with Databricksĭatabricks offers a unified data analytics platform for big data analytics and machine learning used by thousands of customers worldwide. This is the first part of a series of blog posts on working with large volumes of geospatial data. ![]() In this blog post, we give an overview of general approaches to deal with the two main challenges listed above using the Databricks Unified Data Analytics Platform. Unstructured data with location references.Geotagged logs, pictures, videos, and social media.OGC web standards such as WCS, WFS, WMS, and WMTS.Remote sensor formats from Hyperspectral, Multispectral, Lidar, and Radar platforms.Geodatabases accessible via JDBC / ODBC connections such as PostgreSQL / PostGIS.Navigational standards such as used by AIS and GPS devices.Raster formats such as ESRI Grid, GeoTIFF, JPEG 2000, and NITF.Vector formats such as GeoJSON, KML, Shapefile, and WKT.There are many different specialized geospatial formats established over many decades as well as incidental data sources in which location information may be harvested: Further, given that scaled data is often required for advanced use cases, the majority of AI-driven initiatives are failing to make it from pilot to production.Ĭompatibility with various spatial formats poses the second challenge. While enterprises have invested in geospatial data, few have the proper technology architecture to prepare these large, complex datasets for downstream analytics. Customer data has been spilling out of existing vertically scaled geo databases into data lakes for many years now due to pressures such as data volume, velocity, storage cost, and strict schema-on-write enforcement. The sheer proliferation of geospatial data and the SLAs required by applications overwhelms traditional storage and processing systems. The first challenge involves dealing with scale in streaming and batch applications. ![]() Despite all these investments in geospatial data, a number of challenges exist. For example, foot-traffic analysis (reference Building Foot-Traffic Insights Dataset) can help determine the best location to open a new store or, in the Public Sector, improve urban planning. Retailers and government agencies are also looking to make use of their geospatial data. Startups and established companies alike are amassing large corpuses of highly contextualized geodata from vehicle sensors to deliver the next innovation in self-driving cars (reference Databricks fuels wejo's ambition to create a mobility data ecosystem). Another rapidly growing industry for geospatial data is autonomous vehicles. Maps leveraging geospatial data are used widely across industry, spanning multiple use cases, including disaster recovery, defense and intel, infrastructure and health services.įor example, numerous companies provide localized drone-based services such as mapping and site inspection (reference Developing for the Intelligent Cloud and Intelligent Edge). This boom of geospatial big data combined with advancements in machine learning is enabling organizations across industry to build new products and capabilities. ![]() Every day billions of handheld and IoT devices along with thousands of airborne and satellite remote sensing platforms generate hundreds of exabytes of location-aware data. The evolution and convergence of technology has fueled a vibrant marketplace for timely and accurate geospatial data.
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