SQLBucket

.. image:: https://travis-ci.org/socialpoint-labs/sqlbucket.svg?branch=master :target: https://travis-ci.org/socialpoint-labs/sqlbucket

SQLBucket is a lightweight framework to help write, orchestrate and validate SQL data pipelines. It gives the possibility to set variables and introduces some control flow using the fantastic Jinja2 library. It also implements a very simplistic unit and integration test framework where you can validate the results of your ETL in the form of SQL checks. With SQLBucket, you can apply TDD principles when writing data pipelines.

It can work as a stand alone service, or be part of your workflow manager environment (Airflow, Luigi, ..).

Installing

Install and update using pip_:

.. code-block:: text

pip install -U sqlbucket

SQLBucket now works for python 3.10.

A Simple Example

To start working, you need to instantiate your SQLBucket core object with the project_folder parameter. That folder will contain all your SQL ETL. The python file where you create your SQLBucket object is also a good place to instantiate your command line interface, as shown below.

.. code-block:: python

# my_sqlbucket.py
from sqlbucket import SQLBucket


bucket = SQLBucket(projects_folder='projects')


if __name__ == '__main__':
    bucket.cli()

The following command will create your first project in your projects folder.

.. code-block:: bash

python my_sqlbucket.py create-project -n my_first_project

For more info on CLI, please refer to its documentations_.

.. _its documentations: https://github.com/socialpoint-labs/sqlbucket/blob/master/documentation/cli.rst

Your projects should now look like the structure below:

.. code-block:: bash

projects/
    |-- my_first_project/
        |-- config.yaml
        |-- queries/
            |-- query_one.sql
            |-- query_two.sql
        |-- integrity/
            |-- integrity_one.sql

SQLBucket project structure

An SQLBucket project is made of 3 core components: the configuration, the ETL queries and the integrity check queries.

Configuration

The config.yaml is the core of your project. This is where you can define variables at project level, and configure the order your sql queries must be executed. For a better explanations on how to configure variables you can refer to the usage documentation, and also the variables documentation which also describes environment and connections variables.

.. _usage documentation: https://github.com/socialpoint-labs/sqlbucket/blob/master/documentation/usage.rst .. _variables documentation: https://github.com/socialpoint-labs/sqlbucket/blob/master/documentation/variables.rst

ETL queries

The queries folder simply contain your SQL queries. You can organize them in the folder structure of your choice. As long as they are in the queries folder, SQLBucket will find them and execute them when configured to do so. See the documentation on how to write SQL with SQLBucket_.

.. _how to write SQL with SQLBucket: https://github.com/socialpoint-labs/sqlbucket/blob/master/documentation/usage.rst

Integrity queries

The integrity folder simply contain SQL queries to help you validate your ETL. You can organize them in the folder structure of your choice. The only convention is to return the result of your integrity (True/False) in a field named passed. The main idea is that integrity is done by SQL itself. Check documentation on integrity_ for a more detailed explanation on testing the integrity of your ETL. We also have a set of common macros_ that can be helpful to start with.

.. _documentation on integrity: https://github.com/socialpoint-labs/sqlbucket/blob/master/documentation/integrity.rst .. _common macros: https://github.com/socialpoint-labs/sqlbucket/blob/master/documentation/integrity_macros.rst

See below a full example that will actually first run your ETL, and then run your integrity checks for a given database configuration.

.. code-block:: python

from sqlbucket import SQLBucket

connections = {
    'db_demo': 'postgresql://user:password@host:5439/database'
}

bucket = SQLBucket(connections=connections)
project = bucket.load_project(
    project_name='my_first_project',
    connection_name='db_demo',
    variables={'foo': 1}
)

# to run ETL
project.run()

# to run integrity
project.run_integrity()

We recommend setting your connection urls as environment variables for security purposes.

Template project

To get you up to speed, you can create a fork of the SQLBucket template project_ and start building SQL data pipelines within minutes.

.. _SQLBucket template project: https://github.com/philippe2803/sqlbucket-template

Contributing

For guidance on how to make a contribution to SQLBucket, see the contributing guidelines_.

.. _contributing guidelines: https://github.com/socialpoint-labs/sqlbucket/blob/master/CONTRIBUTING.rst

Links

• License: MIT <https://github.com/socialpoint-labs/sqlbucket/blob/master/LICENSE>_
• Releases: https://pypi.org/project/sqlbucket/
• Code: https://github.com/socialpoint-labs/sqlbucket
• Issue tracker: https://github.com/socialpoint-labs/sqlbucket/issues

.. _pip: https://pip.pypa.io/en/stable/quickstart/