Operational Metadata & Lineage

1. Introduction

Operational metadata and lineage are critical components in data engineering, particularly in data governance and quality management. They provide insights into the data lifecycle, tracking its origin, transformations, and usage.

2. Key Concepts

3. Importance

Understanding operational metadata and lineage is essential for:

• Data Governance: Ensures compliance with regulations.
• Data Quality: Identifies issues and improves data trustworthiness.
• Impact Analysis: Helps in assessing the effects of data changes.

4. Implementation on AWS

Implementing operational metadata and lineage on AWS involves using services such as:

• AWS Glue: For data cataloging and ETL processes.
• AWS Lake Formation: For managing data lakes and access control.

4.1 Step-by-Step Implementation

Here’s a simple flowchart illustrating the implementation process:

graph TD;
    A[Start] --> B[AWS Glue Data Catalog];
    B --> C[Define Data Sources];
    C --> D[ETL Jobs Creation];
    D --> E[Data Lineage Tracking];
    E --> F[Data Governance];
    F --> G[End];
    

5. Best Practices

Here are some best practices:

• Regularly audit metadata for consistency.
• Implement automated lineage tracking.
• Ensure accessibility of metadata for users.

6. FAQ