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