A journey of optimization of cloud-based geospatial data processing (opens in new tab)

I share the same feelings, hence I along with OP, made this attempt of creating a library based on whatever seemed most performant and closest to regular data engineering world.

1. Geoparquet for metadata (iceberg catalog could be added later if scale is extremely large) 2. Cloud Optimized Geotiff (COG) for image data (which is what NASA and ESA have been pumping for last 5 years and still do) 3. and an efficient lightweight library that can quickly grab pieces of data from 100s or 1000s of raster files parallely.

I did not want to attempt creating another format, coz I felt that we as geocommunity are at a stage where such tools and files put together forms a pretty good first level foundation to build on. No need for heavy legacy tools, closer to regular data (with parquet) and closer to duckdb world.

I feel adoption may be good, if this is the thesis / guiding principles.

Baby steps surely, but good steps I believe, and this is all thanks to the open source community.

Hi, Sid here from Terrafloww, essentially this blog and library's attempt is based on the fact that COG has emerged as the standard format used by NASA and ESA and others over the past 5 years. And there is way more data in COG and continually being produced as COGs still. So we wanted to make sure that we use the most prevalent format as efficiently and quickly as possible. We didn't try to create a new format, I believe thats best left to the large and open community itself to decide.

Thanks for the question! Happy to answer more.

Totally agree. When I worked at Radiant Earth the most frequent question we got was "I have this data I want to share, how do I create a good STAC for it?"

It was a totally valid question but one that's practically impossible to answer. As a result there's just so much variation between STACs and you never really know what you're going to get.

Agree on both fronts! STAC is pretty complex. My attempt here was to make raw data access easy and fast, not to solve STAC, which I believe stac-geoparquet basically makes an attempt to fix (makes it columnar and hence faster to query at scale).

And yes, having a parquet will add overhead of needing some form of catalog. But I believe we are very close to having Iceberg with native geo types being that catalog. at the same time, it opens another can of worms (databricks and other catalogs etc).

silver lining is that parquet (geoparquet) makes geo data closer to regular data.

Thanks! I will be posting more on this, especially as we try out more use cases like ML dataset curation, globally distributed time series analysis, and more. Surely will take your feedback into account of having variations in AOI sizes. Here;s the terrafloww linkedin, you can find me on it too, https://www.linkedin.com/company/terrafloww-inc/