Exploring and charting data inside a hosted notebook

Colab Enterprise provides a managed Jupyter notebook environment integrated into Google Cloud that lets you analyze and visualize data directly against BigQuery, Cloud Storage, and other GCP data sources without managing infrastructure. It combines the familiar Colab notebook interface with enterprise features like IAM-based access control, Vertex AI integration, and shared runtime templates for consistent compute environments.

1 · Learn the must-know

• 1Colab Enterprise notebooks run on Google-managed or custom runtime templates, allowing teams to standardize compute resources (CPU/GPU/memory) and control costs centrally.
• 2Colab Enterprise is accessed through the Vertex AI section of Google Cloud console and uses IAM permissions, so notebook access and BigQuery/storage access follow standard Google Cloud identity controls rather than notebook-level passwords.
• 3You can query BigQuery directly from a notebook cell using the BigQuery client library (google.cloud.bigquery) or the %%bigquery magic, then load results into a pandas DataFrame for visualization with libraries like matplotlib or seaborn.
• 4Notebooks can be shared and co-edited similarly to Google Docs, with version history, and results/output cells are saved with the notebook for collaboration and reproducibility.
• 5Colab Enterprise integrates with Vertex AI, so notebooks can also be used for ML model prototyping, training, and calling generative AI APIs, not just data exploration.
• 6A common gotcha: BigQuery query costs are incurred each time a notebook cell queries a table, so exam scenarios may test awareness of using LIMIT, sampling, or cached results to control cost during exploratory analysis.

2 · Check your understanding