BigQuery tap class.
Built with the Meltano Singer SDK.
catalog
state
discover
about
stream-maps
schema-flattening
batch
Setting | Required | Default | Description |
---|---|---|---|
project_id | True | None | GCP Project |
credentials_path | False | None | The path to the service account credentials file. |
filter_schemas | False | None | If an array of schema names is provided, the tap will only process the specified BigQuery schemas and ignore others. If left blank, the tap automatically determines ALL available BigQuery schemas. |
stream_maps | False | None | Config object for stream maps capability. For more information check out Stream Maps. |
stream_map_config | False | None | User-defined config values to be used within map expressions. |
flattening_enabled | False | None | 'True' to enable schema flattening and automatically expand nested properties. |
flattening_max_depth | False | None | The max depth to flatten schemas. |
batch_config | False | None |
A full list of supported settings and capabilities is available by running: tap-bigquery --about
(meltanolabs-tap-bigquery-py3.9) Patricks-MBP:tap-bigquery pnadolny$ poetry run tap-bigquery --about --format=markdown
Google BigQuery tap.
Built with the Meltano Singer SDK.
catalog
state
discover
about
stream-maps
schema-flattening
batch
Setting | Required | Default | Description |
---|---|---|---|
project_id | True | None | GCP Project |
credentials_path | False | None | The path to the service account credentials file. |
filter_schemas | False | None | If an array of schema names is provided, the tap will only process the specified BigQuery schemas and ignore others. If left blank, the tap automatically determines ALL available BigQuery schemas. |
stream_maps | False | None | Config object for stream maps capability. For more information check out Stream Maps. |
stream_map_config | False | None | User-defined config values to be used within map expressions. |
flattening_enabled | False | None | 'True' to enable schema flattening and automatically expand nested properties. |
flattening_max_depth | False | None | The max depth to flatten schemas. |
batch_config | False | None |
A full list of supported settings and capabilities is available by running: tap-bigquery --about
This Singer tap will automatically import any environment variables within the working directory's
.env
if the --config=ENV
is provided, such that config values will be considered if a matching
environment variable is set either in the terminal context or in the .env
file.
You can easily run tap-bigquery
by itself or in a pipeline using Meltano.
tap-bigquery --version
tap-bigquery --help
tap-bigquery --config CONFIG --discover > ./catalog.json
Follow these instructions to contribute to this project.
pipx install poetry
poetry install
Create tests within the tests
subfolder and
then run:
poetry run pytest
You can also test the tap-bigquery
CLI interface directly using poetry run
:
poetry run tap-bigquery --help
Testing with Meltano
Note: This tap will work in any Singer environment and does not require Meltano. Examples here are for convenience and to streamline end-to-end orchestration scenarios.
Next, install Meltano (if you haven't already) and any needed plugins:
# Install meltano
pipx install meltano
# Initialize meltano within this directory
cd tap-bigquery
meltano install
Now you can test and orchestrate using Meltano:
# Test invocation:
meltano invoke tap-bigquery --version
# OR run a test `elt` pipeline:
meltano elt tap-bigquery target-jsonl
See the dev guide for more instructions on how to use the SDK to develop your own taps and targets.