This is a collection of python applications and a helper docker image used to index data into a datacube using odc-tools.

The functionality is exposed in form of various <storage backend>-to-dc utilities which accept URI/GLOB parameters and product name(s) to index into a default datacube. These utilities include:

1. bootstrap-odc.sh: Shell script to consume URL based metadata and product catalogs and bootstrap a datacube.
2. s3-to-dc: Index from S3 storage to a Datacube database.
3. thredds-to-dc: Index from Thredds server to a Datacube database.
4. sqs-to-dc: Index from SQS queue to a Datacube database.
5. stac-to-dc: Index from a STAC API into a Datacube database.

It has code to perform the follow steps:

1. Crawl S3 to find datasets using s3-find and produce a generator.
2. Crawl Thredds using Thredds Crawler with NCI specific defaults (overrideable).
3. Index dataset YAML's found into datacube using generator/list equivalent of dc-index-from-tar while skipping the tar file.

Production deployments of OpenDataCube typically have follow on steps to a new product or new datasets for an existing product getting indexed. These steps are outlined below:

1. Use OWS Update ranges to update layer extents for products in OWS managed tables in a separate container.
2. Use Explorer Summary generation to generate summaries.
3. The 3-containers are tied together by an Airflow DAG using a K8S Executor.
4. Utilities in the 3 parts of the datacube applications/library ecosystem are tied together by custom Python scripts.