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Notebooks Overview

Notebooks in OpenMetadata represent interactive computational documents that combine code, visualizations, and narrative text. Notebooks are essential tools for data exploration, analysis, machine learning development, and collaborative data science.

What are Notebooks?

Notebooks provide an interactive environment where data professionals can:

  • Explore Data: Query databases, files, and APIs interactively
  • Develop Models: Build and train machine learning models
  • Create Visualizations: Generate charts and graphs inline
  • Document Analysis: Combine code with explanatory text and findings
  • Share Insights: Collaborate with teams on data analysis
  • Prototype Pipelines: Develop and test data transformations

Notebook Types

OpenMetadata supports various notebook platforms:

Jupyter Notebooks

  • Industry-standard notebooks for Python, R, Julia
  • Rich visualization ecosystem
  • Extensive library support
  • Local and cloud-hosted (JupyterHub, JupyterLab)

Databricks Notebooks

  • Enterprise notebooks on Databricks platform
  • Integrated with Apache Spark
  • Collaborative editing
  • Production-ready data pipelines

Google Colab

  • Cloud-based Jupyter notebooks
  • Free GPU/TPU access
  • Easy sharing and collaboration
  • Integration with Google Drive

Apache Zeppelin

  • Web-based notebooks for big data
  • Multi-language support
  • Built-in visualization
  • Integration with Hadoop ecosystem

Azure Notebooks

  • Cloud notebooks on Microsoft Azure
  • Integration with Azure services
  • Scalable compute resources

Amazon SageMaker Notebooks

  • AWS-managed Jupyter notebooks
  • ML-optimized environments
  • Integration with SageMaker services

Notebook Entities

graph TB
    A[Notebook Service] --> B[Notebooks]
    B --> C1[Cells]
    B --> C2[Execution History]
    B --> C3[Dependencies]

    C1 --> D1[Code Cells]
    C1 --> D2[Markdown Cells]
    C1 --> D3[Output Cells]

    style A fill:#667eea,color:#fff
    style B fill:#4facfe,color:#fff
    style C1 fill:#00f2fe,color:#333
    style C2 fill:#00f2fe,color:#333
    style C3 fill:#00f2fe,color:#333
    style D1 fill:#ffd700,color:#333
    style D2 fill:#ffd700,color:#333
    style D3 fill:#ffd700,color:#333

Notebook Metadata

OpenMetadata captures:

  • Notebook Properties: Name, path, version, last execution
  • Cell Structure: Code cells, markdown cells, outputs
  • Data Dependencies: Tables, files, APIs accessed by the notebook
  • Compute Resources: Kernel type, cluster configuration
  • Execution History: Run history, execution times, results
  • Collaboration: Authors, contributors, reviewers
  • Lineage: Data sources used and targets created
  • Tags & Classification: Organization and discovery

Use Cases

Data Exploration

Analysts use notebooks to: - Query databases interactively - Explore data distributions - Identify data quality issues - Prototype analyses

ML Development

Data scientists use notebooks to: - Feature engineering - Model training and evaluation - Hyperparameter tuning - Experiment tracking

Reporting & Analytics

Teams use notebooks for: - Ad-hoc analysis - Business intelligence - Automated reporting - Data storytelling

ETL Development

Engineers use notebooks to: - Develop data transformations - Test pipeline logic - Prototype workflows - Debug data issues

Education & Documentation

Organizations use notebooks for: - Training materials - Code documentation - Best practices sharing - Reproducible research

Notebook Governance

Ownership & Access

  • Clear ownership assignment
  • Access control and permissions
  • Sharing policies
  • Version control integration

Quality & Standards

  • Code review processes
  • Testing requirements
  • Documentation standards
  • Naming conventions

Lineage Tracking

  • Track data sources used
  • Identify downstream consumers
  • Understand impact of changes
  • Map dependencies

Security & Compliance

  • Credential management
  • PII data handling
  • Audit logging
  • Encryption requirements

Integration Points

Notebooks integrate with:

graph LR
    A[Notebook] --> B1[Databases]
    A --> B2[Data Lakes]
    A --> B3[ML Platforms]
    A --> B4[BI Tools]
    A --> B5[Version Control]

    B1 -.->|queries| C1[Tables]
    B2 -.->|reads| C2[Files]
    B3 -.->|trains| C3[ML Models]
    B4 -.->|generates| C4[Reports]
    B5 -.->|stored in| C5[Git Repos]

    style A fill:#4facfe,color:#fff,stroke:#4c51bf,stroke-width:3px
    style B1 fill:#764ba2,color:#fff
    style B2 fill:#764ba2,color:#fff
    style B3 fill:#764ba2,color:#fff
    style B4 fill:#764ba2,color:#fff
    style B5 fill:#667eea,color:#fff
    style C1 fill:#00f2fe,color:#333
    style C2 fill:#00f2fe,color:#333
    style C3 fill:#00f2fe,color:#333
    style C4 fill:#00f2fe,color:#333
    style C5 fill:#00f2fe,color:#333

Best Practices

1. Version Control

Store notebooks in Git repositories for versioning and collaboration.

2. Modular Design

Break complex analyses into reusable functions and libraries.

3. Document Thoroughly

Use markdown cells to explain logic, assumptions, and findings.

4. Parameterize Notebooks

Make notebooks reusable with parameters instead of hardcoding values.

5. Test Code

Include assertions and tests to validate results.

6. Manage Dependencies

Document required libraries and versions.

7. Clean Outputs

Clear outputs before committing to version control.

8. Track Data Lineage

Document data sources and transformations clearly.

  • Notebook: Individual notebook entity specification
  • Table: Database tables accessed by notebooks
  • File: Data files used in notebooks
  • ML Model: Models trained in notebooks
  • Pipeline: Pipelines developed from notebooks
  • User: Notebook authors and contributors

Next Steps