Have you ever struggled with large amounts of geospatial data, huge volumes of files and many custom formats? Have you spent hours — even days — converting and wrangling disparate data formats and wondering how to combine data from different sources? Then this talk is for you!
The solution? Forget about files or force-fitting geospatial data into tabular databases. Imagine a solution that naturally shape-shifts to the underlying data structure. TileDB is this solution: a multimodal database based on multi-dimensional arrays with which you can model any data type. TileDB is architected around a storage engine that uses arrays to store any data type, morphs into specialized analysis applications, supports a range of indexing options, and features an analysis-ready format designed for cloud object storage.
TileDB supports all geospatial data in a unified way with numerous APIs and integrates well with compute and visualization tools. Large datasets can be stored on multiple backends ranging from a local filesystem to cloud storage providers such as Amazon S3. TileDB has integrations with many tools that already exist within Python, such as Dask, Xarray and pandas, as well as with geospatial-specific frameworks, such as PDAL for point clouds and GDAL for raster and geometry data. We also build interactive visualization tools with the BabylonJS gaming engine that streams geospatial data directly from TileDB arrays. This makes TileDB a natural fit for geospatial datasets!
When all your data fits naturally into your database’s underlying data structures, it becomes much easier to work with. In this talk I will show examples of how to efficiently work with very large geospatial datasets such as global Sentinel-2 satellite data and global Overture open map data. I will show how we ingested these large datasets into arrays in parallel and the applications we build on top. I will cover how to ingest, load, analyze and visualize all types of geospatial data and show how to combine and use them together.
Come along to this talk to learn how to get started with geospatial data and TileDB from practical examples.
Technical Level: Technical practitioner
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