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bigquery-etl
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Bigquery ETL
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Python 94.9%
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Eduardo Filho 214d062b5b
Complete backfill of geckoview_versions (#5742) 		2024-06-05 11:38:25 -04:00
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bigquery_etl Added dataset id (#5721) 2024-06-04 13:16:21 -07:00
docs DENG-3851 Updated managed backfill docs with slack channel for notifcations (#5630) 2024-05-21 12:36:14 -07:00
script GLAM fix replace_dataset to account for already fully qualified datasets in query (#5517) 2024-05-07 18:52:30 -04:00
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sql_generators Exclude `accounts_cirrus` Glean app from baseline ping checks as it has no baseline pings (bug 1888718). (#5726) 2024-06-04 08:33:34 -07:00
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README.md

CircleCI

BigQuery ETL

This repository contains Mozilla Data Team's:

  • Derived ETL jobs that do not require a custom container
  • User-defined functions (UDFs)
  • Airflow DAGs for scheduled bigquery-etl queries
  • Tools for query & UDF deployment, management and scheduling

For more information, see https://mozilla.github.io/bigquery-etl/

Quick Start

Pre-requisites

  • Pyenv (optional) Recommended if you want to install different versions of python, see instructions here. After the installation of pyenv, make sure that your terminal app is configured to run the shell as a login shell.
  • Homebrew (not required, but useful for Mac) - Follow the instructions here to install homebrew on your Mac.
  • Python 3.11+ - (see this guide for instructions if you're on a mac and haven't installed anything other than the default system Python).

GCP CLI tools

  • For Mozilla Employees (not in Data Engineering) - Set up GCP command line tools, as described on docs.telemetry.mozilla.org. Note that some functionality (e.g. writing UDFs or backfilling queries) may not be allowed. Run gcloud auth login --update-adc to authenticate against GCP.
  • For Data Engineering - In addition to setting up the command line tools, you will want to log in to shared-prod if making changes to production systems. Run gcloud auth login --update-adc --project=moz-fx-data-shared-prod (if you have not run it previously).

Installing bqetl

  1. Clone the repository
git clone git@github.com:mozilla/bigquery-etl.git
cd bigquery-etl
  1. Install the bqetl command line tool
./bqetl bootstrap
  1. Install standard pre-commit hooks
venv/bin/pre-commit install

Finally, if you are using Visual Studio Code, you may also wish to use our recommended defaults:

cp .vscode/settings.json.default .vscode/settings.json
cp .vscode/launch.json.default .vscode/launch.json

And you should now be set up to start working in the repo! The easiest way to do this is for many tasks is to use bqetl. You may also want to read up on common workflows.

Releasing a new version of bqetl

To push a new version of bqetl to PyPI, update the version in pyproject.toml. The version numbers follow the CalVer scheme, with the Micro version numbers starting at 1. For example, for the first package version getting published in March 2024, the version would be 2024.3.1.