SQL Pipelines

Ploomber comes with built-in support for SQL. You provide SQL scripts and Ploomber manages connections to the database and orchestrates execution for you.

graph LR ca[Clean table A] --> ta[Transform] --> m[Merge] cb[Clean table B] --> tb[Transform] --> m m --> Dump --> Plot


Check out our JupySQL library. It allows you to run SQL in a Jupyter notebook: result = %sql SELECT * FROM table

Process with SQL and Python

With data warehouses such as Snowflake, using SQL for transforming data can significantly simplify the development process since the warehouse takes care of scaling your code.

You can use Ploomber and SQL to process large datasets quickly, then download the data to continue your analysis with Python for plotting or training a Machine Learning model

Example: BigQuery and Cloud Storage pipeline
pip install ploomber
ploomber examples -n templates/google-cloud -o google-cloud
Example: SQL pipeline (transform with SQL, and plot with Python)
pip install ploomber
ploomber examples -n templates/spec-api-sql -o spec-api-sql

Uploading batch predictions to a database

If you’re working on a Machine Learning whose predictions must be uploaded to a database table, you can implement this with Ploomber.


Ploomber allows you to write ETL SQL pipelines.

Example: ETL pipeline
pip install ploomber
ploomber examples -n templates/etl -o etl