feat: Dask parallel processing support #10
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Enable parallel chunk processing with Dask distributed: - Add --dask-cluster flag to conversion workflow - Update to v26 image with Dask support - Add validation task between convert and register stages Initial test shows 1.6× speedup (320s vs 516s baseline).
Task was defined but never referenced in DAG (lines 25-37).
Add workflow parameters: - stac_api_url, raster_api_url (API endpoints) - s3_endpoint, s3_output_bucket, s3_output_prefix (S3 config) Replace all hardcoded values with parameter references for: - STAC/raster API URLs in register/augment tasks - S3 endpoint in all tasks - S3 bucket/prefix in convert/validate/register tasks Enables easy environment switching (dev/staging/prod) via parameter override.
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Enables optional Dask distributed computing for large-scale GeoZarr conversions with automatic fallback to single-node mode.
Changes
dask[distributed]viaeopf-geozarrdependencyPerformance
Single-node mode suitable for most datasets (<10GB). Dask mode enables 3-5x speedup for large scenes when cluster available.
Testing
uv run pytest tests/unit/test_augment_stac_item.py -v docker run data-pipeline:v26 python3 -c "import dask.distributed"Impact
Backward compatible with existing workflows. Ready for future Dask cluster deployment without code changes.