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Project description
airbyte_databricks_cache
Databricks cache implementation for PyAirbyte
Installation
pip install airbyte # pre-requisite
pip install airbyte-databricks-cache
Usage
import airbyte as ab
import airbyte_databricks_cache # Must import to inject the module into airbyte.caches.databricks
from airbyte.caches.databricks import DatabricksCache # pylint: disable=E0401:import-error
# create airbyte source
source = ab.get_source(...)
# create DatabricksCache
cache_dbks = DatabricksCache(
access_token = ab.get_secret("databricks_access_token"),
server_hostname = ab.get_secret("databricks_server_hostname"),
http_path= ab.get_secret("databricks_http_path"),
catalog = ab.get_secret("databricks_catalog"),
schema_name = ab.get_secret("databricks_target_schema"),
staging_volume_w_location = ab.get_secret("databricks_staging_volume_w_location")
)
# read into cache
result = source.read(
cache=cache_dbks,
streams=['xxx'],
force_full_refresh=False,
write_strategy="append"
)
### OR
# write to destination
destination = ab.get_destination("destination-databricks", cache_dbks)
destination.write(source, ...)
Build and deploy
Happens via github workflow.
If building locally, manually:
poetry run python build.py
poetry build --format wheel
Run locally to test that import works:
poetry run python -c "import airbyte_databricks_cache"
Release mechanics:
# release
git fetch --tags origin
git describe --tags --abbrev=0
gh release create v0.1.8 --generate-notes
# gh release create v0.1.8 --target feat-workflow --generate-notes
git fetch --tags origin
# delete local tags if remote is deleted
git fetch origin --prune --prune-tags
## manual addons
# check long desc for pypi
twine check dist/*
Project details
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