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How to use Deepnote for ETL data pipelines

By Nick Barth

Updated on March 6, 2024


Deepnote is a powerful collaborative data science notebook designed to make data analysis, transformation, and pipeline creation easier. In this guide, we'll walk you through the process of using Deepnote for ETL (Extract, Transform, Load) and pipeline development. This tutorial is tailored for data analysts and data engineers looking to leverage Deepnote's features to streamline their workflows.

Importing and integrating data sources

One of the first steps in any ETL process is importing data. Deepnote makes it easy to connect to various data sources:

  • Click on the Integrations tab in the sidebar.
  • Select the data source you want to integrate (e.g., Google Sheets, SQL databases, CSV files).
  • Follow the prompts to authorize and connect your data source.

Crafting and executing ETL procedures

With your data imported, you can start crafting your ETL procedures using Deepnote's code cells and integrated libraries such as Pandas, NumPy, and SQLAlchemy:

Extract data:

Write code to extract data from your connected sources.

data = pd.read_csv('path_to_your_file.csv')

Transform data:

Clean, filter, and transform your data to suit your needs.⁠

data = data.dropna()
data['new_column'] = data['existing_column'].apply(lambda x: x * 2)

Load data:

Load your transformed data into a destination such as a database or another file.

Utilizing version control

Managing changes and iterations in your ETL pipelines is crucial. Deepnote provides version control features to help with this:

  • Access the History tab in the sidebar to view and revert to previous versions of your notebook.
  • Use comments to annotate significant changes and iterations.

Automating pipeline execution

Deepnote allows you to automate the execution of your ETL pipelines using scheduling and deployment features:

  1. Scheduling:
    • Click on the Schedule button in the top right corner of your notebook.
    • Set up a schedule for your notebook to run at specified intervals (e.g., daily, weekly).
  2. Deployment:
    • Deploy your notebook as a data app or API to automate data workflows.

Monitoring pipeline performance and debugging

Ensuring your ETL pipelines run smoothly requires monitoring and debugging:

  1. Logs and output:
    • Monitor the logs and output of your code cells to catch any errors or issues.
    • Use print statements and logging libraries to debug your code.
  2. Performance metrics:
    • Track the performance of your ETL processes by measuring execution time and resource usage.
    • Optimize your code as needed to improve efficiency.


Deepnote provides a comprehensive and user-friendly platform for developing and managing ETL pipelines. By following the steps outlined in this guide, you can import data, craft ETL procedures, utilize version control, automate execution, and monitor performance effectively.

Ready to take your data workflows to the next level? Start leveraging Deepnote today, and don't hesitate to reach out to our support team if you have any questions or need assistance.

Happy data wrangling!

Nick Barth

Product Engineer

Nick has been interested in data science ever since he recorded all his poops in spreadsheet, and found that on average, he pooped 1.41 times per day. When he isn't coding, or writing content, he spends his time enjoying various pursuits leisure.

Follow Nick on LinkedIn and GitHub

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