Refactored Space Cowboy

Databricks Tagging Strategies

A comprehensive guide to implementing effective tagging strategies in Databricks environments.

Overview

The tagging_strategies.ipynb notebook provides practical implementations and examples for utilizing Databricks' three distinct tagging systems:

1. Resource-level tagging: For attributing compute costs to teams, projects, or users
2. Unity Catalog securable object tagging: For organizing, classifying, and governing data assets
3. Serverless compute workload tagging: For tracking usage of serverless resources

Features

• Python Implementations: Ready-to-use Python functions for applying tags to various Databricks resources
• Tag Management: Methods for adding, updating, and removing tags across different resource types
• Resource Optimization: Best practices for using tags to manage compute costs and resource allocation
• Data Governance: Techniques for classifying and organizing data assets in Unity Catalog
• API Integration: Examples of interacting with Databricks REST APIs for tagging operations

Prerequisites

• An active Databricks workspace
• Python 3.7+
• PySpark
• Access to Databricks REST APIs
• Required permissions for managing the resources you want to tag

Getting Started

1. Clone this repository:

git clone https://github.com/shawndeggans/refactored_space_cowboy.git

2. Navigate to the project directory:

cd refactored_space_cowboy/budget_nb

3. Install the required dependencies:

pip install python-dotenv requests

4. Create a .env file with your Databricks credentials:

TOKEN=your_databricks_token
DATABRICKS_INSTANCE=your_databricks_instance
CLUSTER_ID=your_cluster_id_for_testing
WAREHOUSE_ID=your_warehouse_id_for_testing

5. Import the notebook into your Databricks workspace.

Resource Tagging Examples

The notebook includes examples for tagging various Databricks resources:

• Clusters: Tag all-purpose and job clusters with custom attributes
• SQL Warehouses: Apply tags to SQL warehouses for cost tracking
• Unity Catalog Objects: Tag catalogs, schemas, tables, and columns
• Tag Management: Add, update, and remove tags programmatically

Best Practices

• Use consistent tagging conventions across your organization
• Apply tags at multiple levels (workspace, cluster, table, etc.) for comprehensive tracking
• Leverage tags for budget forecasting and cost optimization
• Implement tags as part of your data governance strategy
• Automate tag application through CI/CD pipelines

Contact

For questions or collaboration opportunities, please contact Shawn Deggans:

• GitHub: @shawndeggans