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Mosaic: A Framework for Geospatial Analytics at Scale

On Demand

Type

  • Session

Format

  • Hybrid

Track

  • Industry and Business Use Cases

Difficulty

  • Intermediate

Room

  • Moscone South | Level 2 | 211

Duration

  • 35 min
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Overview

In this session we’ll present Mosaic, a new Databricks Labs project with a geospatial flavour.



Mosaic provides users of Spark and Databricks with a unified framework for distributing geospatial analytics. Users can choose to employ existing Java-based tools such as JTS or Esri's Geometry API for Java and Mosaic will handle the task of parallelizing these tools' operations: e.g. efficiently reading and writing geospatial data and performing spatial functions on geometries. Mosaic helps users scale these operations by providing spatial indexing capabilities (using, for example, Uber's H3 library) and advanced techniques for optimising common point-in-polygon and polygon-polygon intersection operations.



The development of Mosaic builds upon techniques developed with Ordnance Survey (the central hub for geospatial data across UK Government) and described in this blog post: https://databricks.com/blog/2021/10/11/efficient-point-in-polygon-joins-via-pyspark-and-bng-geospatial-indexing.html

Session Speakers

Stuart Lynn

Databricks

Milos Colic

Databricks

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