55 строки
3.1 KiB
Markdown
55 строки
3.1 KiB
Markdown
[](https://dl.circleci.com/status-badge/redirect/gh/mozilla/bigquery-etl/tree/main)
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# BigQuery ETL
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This repository contains Mozilla Data Team's:
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- Derived ETL jobs that do not require a custom container
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- User-defined functions (UDFs)
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- Airflow DAGs for scheduled bigquery-etl queries
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- Tools for query & UDF deployment, management and scheduling
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For more information, see [https://mozilla.github.io/bigquery-etl/](https://mozilla.github.io/bigquery-etl/)
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## Quick Start
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### Pre-requisites
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- **Pyenv** (optional) Recommended if you want to install different versions of python, see instructions [here](https://github.com/pyenv/pyenv#basic-github-checkout). After the installation of pyenv, make sure that your terminal app is [configured to run the shell as a login shell](https://github.com/pyenv/pyenv/wiki/MacOS-login-shell).
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- **Homebrew** (not required, but useful for Mac) - Follow the instructions [here](https://brew.sh/) to install homebrew on your Mac.
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- **Python 3.11+** - (see [this guide](https://docs.python-guide.org/starting/install3/osx/) for instructions if you're on a mac and haven't installed anything other than the default system Python).
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### GCP CLI tools
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- **For Mozilla Employees (not in Data Engineering)** - Set up GCP command line tools, [as described on docs.telemetry.mozilla.org](https://docs.telemetry.mozilla.org/cookbooks/bigquery/access.html#using-the-bq-command-line-tool). 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.
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- **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).
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### Installing bqetl
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1. Clone the repository
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```bash
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git clone git@github.com:mozilla/bigquery-etl.git
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cd bigquery-etl
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```
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2. Install the `bqetl` command line tool
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```bash
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./bqetl bootstrap
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```
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3. Install standard pre-commit hooks
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```bash
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venv/bin/pre-commit install
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```
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Finally, if you are using Visual Studio Code, you may also wish to use our recommended defaults:
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```bash
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cp .vscode/settings.json.default .vscode/settings.json
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cp .vscode/launch.json.default .vscode/launch.json
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```
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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`](https://mozilla.github.io/bigquery-etl/bqetl/). You may also want to read up on [common workflows](https://mozilla.github.io/bigquery-etl/cookbooks/common_workflows/).
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## Releasing a new version of `bqetl`
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To push a new version of `bqetl` to [PyPI](https://pypi.org/project/mozilla-bigquery-etl/), update the `version` in [`pyproject.toml`](pyproject.toml). The version numbers follow the [CalVer](https://calver.org/) 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`.
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