Part 11/13:

The presentation shares concrete examples from real-world experience:

• Building AI-powered monitoring tools that automatically detect pipeline anomalies and suggest fixes.

• Creating end-to-end data lineage systems that leverage AI for precise root cause analysis.

• Developing self-service interfaces that empower business users to generate data reports via natural language, reducing dependency on data engineers.

• Automating metadata collection, classification, and data tag enrichment, significantly reducing manual overhead.

• Implementing text-to-SQL solutions that facilitate natural language querying, improving accessibility for non-technical stakeholders.

• Applying synthetic data generation techniques to ensure robust testing environments and adhere to privacy requirements.