Title

AWS re:Invent 2022 - Automated data discovery: Break free from hours of manual data hunting (PRT069)

Summary

• The session focused on the importance of automated data discovery in the face of growing data volumes and disparate data sources.
• Data discovery is becoming crucial due to the explosion of data, decentralization of data ownership, and democratization of data access.
• Challenges include ensuring a single source of truth, consistent KPI definitions, and enabling non-technical stakeholders to understand and utilize data.
• Traditional documentation and open-source solutions like Apache Atlas, DataHub, or Amundsen are not sufficient for growing data needs.
• Proprietary solutions like Enterprise Data Catalogs are often too technical and not user-friendly for non-technical data consumers.
• SelectStar offers a data discovery platform that provides a single source of truth, universal search, context, collaboration, and governance tools.
• Features highlighted include table and column popularity scores, example queries, data lineage, and automated entity relationship diagrams.
• The speaker emphasized the need for automated, easy-to-use tools that serve both technical and non-technical users to make data discovery efficient and accessible.

Insights

• The exponential growth of data and the variety of data sources necessitate a robust data discovery solution to maintain a single source of truth and ensure data quality.
• Decentralization of data ownership and democratization of data access reflect a shift towards self-service analytics, where business users are empowered to make data-driven decisions.
• Discrepancies in KPI definitions across different departments can lead to confusion and misinformed decisions, highlighting the need for standardized data glossaries and definitions.
• The limitations of manual documentation and traditional data cataloging methods underscore the need for automated, scalable solutions that can keep pace with rapid data growth.
• SelectStar's approach to data discovery, focusing on usability, automation, and integration with existing data stacks, suggests a trend towards more user-centric data tools that facilitate collaboration and governance.
• The emphasis on features like popularity scores, data lineage, and automated ER diagrams indicates a growing demand for tools that not only organize data but also provide insights into its usage and relationships.
• The session suggests that the future of data management will increasingly rely on intelligent, automated systems that can adapt to complex and evolving data environments.