This release is a pre-release and may not be stable for production use.
SQLBucket was built to help write, orchestrate and validate SQL ETL. It gives the possibility to set variables and introduces some control flow like if/else and for loops, to make your queries dynamic when writing them. It also implements a very simplistic integration testing framework to validate the results of your ETL pipelines in the form of SQL checks.
Lightweight, it can work as a stand alone service, or be part of your workflow manager environment (Airflow, Luigi, ..).
Installing
Install and update using pip:
pip install -U sqlbucketSQLBucket works only for Python 3.6 and 3.7, and probably 3.8 although not tested yet.
A Simple Example
To start working with SQLBucket, you need to have a ‘projects’ folder that will contain all your SQL ETL. SQLBucket, essentially works with the following folder structure as a root folder, where you can have as many projects as you want.
projects/
|-- project1/
|-- project2/
|-- project3/
...Inside a project, it must contain a folder called queries that will contains your SQL files to be ran, and a config.yaml that will let you set the order in which those queries must be processed. SQLBucket leverages on the amazing Jinja2 templating library to give you the possibility to set variables in your SQL as well as giving you pure execution flows like for loops. To see in more depth what can be done, see the documentation on how to write SQL in SQLBucket.
In practice, this is how a project folder would look.
projects/
|-- my_super_project/
|-- config.yaml
|-- queries/
|-- my_super_insert_query.sql
|-- some_other_query.sql
|-- integrity/
|-- test1.sql
|-- test2.sqlThe integrity folder gives you the possibility to write some checks in SQL, that will ran at the end of your ETL to validate your data. Check documentation on integrity for a more detailed explanation on testing the integrity of your ETL.
This is how you would launch a project:
from sqlbucket import SQLBucket
connections = {
'db_test': 'postgresql://user:password@host:5439/database'
}
bucket = SQLBucket(connections=connections)
project = bucket.load_project(
project_name='fat_etl',
connection_name='db_test',
variables={'foo': 1}
)
project.run()
project.run_integrity()This would trigger logs as below.
You can also launch a project using the sqlbucket command line interface.
Contributing
For guidance on how to make a contribution to SQLBucket, see the contributing guidelines.
Links
License: MIT
Releases: https://pypi.org/project/sqlbucket/
Issue tracker: https://github.com/socialpoint-labs/sqlbucket/issues
3 maintainers
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