Part 7/10:

Beyond infrastructure, Prakas highlighted the integration of AI and ML as a transformational force in data engineering:

• Semantic Cataloging: Employing AI-driven metadata enrichment to understand and interpret data columns, schemas, and business meanings enhances self-service analytics.

• Automated Query Optimization: Leveraging AI to review and optimize queries can improve performance and efficiency.

• AI in Data Quality and Governance: Using AI for anomaly detection, threat identification, and ensuring data lineage maintains trustworthiness.

• Operational AI/ML Pipelines: Building robust ML pipelines with MLOps enables continuous model evaluation, deployment, and iteration to adapt to changing business and market conditions.