Associate Data Practitioner

Use Jupyter notebooks to analyze and visualize data (e.g., Colab Enterprise)

Perform Data Analysis with BigQuery

Jupyter notebooks such as Colab Enterprise let you write and run code interactively. You can mix Python code cells with SQL cells, making it easy to switch between data processing and querying. When you run a SQL cell against BigQuery, the final result is automatically saved as a pandas DataFrame, so you can work with it right away in Python.

In your SQL cells, you can reference Python variables by enclosing their names in { }. For example, {my_threshold} injects a Python value into your query, and {df} treats a previous DataFrame as if it were a BigQuery table. This method helps you build dynamic queries without rewriting code, keeping your work clear and efficient.

Notebooks also let you run multiple SQL statements in one cell, but remember that only the last query will be stored as a DataFrame. This means you can test different queries in the same place but need to focus on the final result for further analysis. Properly organizing SQL cells ensures you don’t lose any intermediate steps.

To use notebooks on Google Cloud, you must assign the right IAM roles. The main roles include:

• Code Owner: full control over the notebook.
• Code Editor: can change code but not share it.
• Code Viewer: can view code and output.
  Users also need the Notebook Runtime User and BigQuery User roles to execute queries. Without these permissions, you cannot run or share your notebook.

Once you have your DataFrame, you can use Python libraries like pandas or matplotlib to visualize and explore your data. You can create charts, compute statistics, and highlight trends all within the same notebook. This seamless workflow helps you uncover insights quickly and share them with your team.

Conclusion

Using Jupyter notebooks on GCP combines the power of interactive coding with BigQuery’s fast SQL engine. You can write Python and SQL side by side, link variables and DataFrames, and store query results automatically. Proper IAM roles keep your data secure and ensure the right users can run and share notebooks. Finally, built-in support for libraries like pandas and matplotlib lets you visualize trends and draw meaningful conclusions from your data.