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2.4. Data Catalogs and Governance

First Principle: Data catalogs and governance for ML fundamentally ensure data discoverability, quality, security, and compliance across the data landscape, enabling trusted access and responsible use of data for ML.

As data volumes grow and more stakeholders need access, managing data assets becomes complex. Data catalogs and governance tools provide mechanisms for organizing, securing, and auditing data for ML.

Key Concepts of Data Catalogs & Governance for ML:
  • Data Discoverability: Making it easy for data scientists to find relevant datasets.
  • Metadata Management: Storing information about data (schema, data types, location, ownership, usage).
  • Access Control: Granularly defining who can access what data.
  • Auditing: Tracking data access and changes for compliance.
  • Compliance: Adhering to regulatory requirements (e.g., GDPR, HIPAA) regarding data usage.
AWS Services for Data Catalogs & Governance in ML:
  • AWS Glue Data Catalog: (A centralized metadata repository.)
    • What it is: A persistent metadata store for your data assets, serving as a unified metadata repository across various AWS services (Athena, EMR, Redshift Spectrum, SageMaker).
    • How it works: Glue Crawlers automatically infer schema and partition information from data in S3 and populate the catalog.
    • Benefits: Enables schema-on-read for data in S3 and provides a central place for data definitions.
  • AWS Lake Formation: (Simplifies building and securing data lakes.)
    • What it is: A service that helps you build, secure, and manage data lakes. It simplifies security management by providing a centralized console to define granular data access policies.
    • Benefits: Integrates with Glue Data Catalog to provide fine-grained access control on tables and columns, simplifying permissions management compared to direct S3 bucket policies.
    • Permissions: You can grant access at the table, column, or row level to IAM users and roles.
  • IAM (Identity and Access Management): (Manages access to AWS services and resources.) Fundamental for controlling who can access data and ML services.
  • Amazon Macie: (Discover and protect sensitive data in S3.) Uses ML to discover, classify, and protect sensitive data in Amazon S3. Helps with compliance.
  • AWS CloudTrail: (Logs API activity.) For auditing data access and changes to data-related resources.

Scenario: Your company stores customer data in an Amazon S3 data lake. Data scientists need access to specific columns of data for training, while analysts only need aggregated views, and sensitive columns (e.g., PII) must be restricted to a few authorized personnel. All data access must be auditable.

Reflection Question: How do data catalogs (AWS Glue Data Catalog) and governance services (AWS Lake Formation for granular access, IAM for roles) fundamentally ensure data discoverability, quality, security, and compliance across the data landscape, enabling trusted and responsible use of data for ML?