Source of this article and featured image is DZone AI/ML. Description and key fact are generated by Codevision AI system.

This article explores how generative AI enhances data lakes by adding semantic intelligence to metadata, transforming static storage into dynamic, searchable systems. It details an architecture combining Apache Iceberg, AWS Glue, and Bedrock to automate metadata tagging and context understanding. The tutorial walks through steps like creating Iceberg tables, using Lambda for AI enrichment, and implementing semantic search with OpenSearch. Author Vivek Venkatesan explains how this approach bridges data engineering and knowledge engineering by making metadata self-descriptive and actionable. It’s worth reading because it addresses the growing need for intelligent data platforms that reduce manual documentation and improve compliance.

Key facts

  • Generative AI adds semantic intelligence to metadata, enabling data lakes to describe, categorize, and connect datasets automatically.
  • The architecture combines Apache Iceberg for structure, AWS Glue for automation, and Amazon Bedrock for AI-driven metadata enrichment.
  • Lambda functions trigger AI models to tag sensitive fields, summarize schemas, and link related datasets across domains.
  • Semantic search with OpenSearch allows analysts to find datasets by conceptual meaning rather than file paths or naming conventions.
  • This approach reduces manual documentation efforts by 60% and enables proactive compliance through automated PII detection.
See article on DZone AI/ML
TAGS: #AI/ML  #Apache Iceberg  #AWS  #Data Engineering  #Data Governance  #data lakes  #Generative AI  #Intelligent Data Platforms  #Metadata Intelligence  #semantic search