Part 2/11:
Historically, enterprises built data warehouses, data lakes, and lakehouses, mostly designed around batch processing paradigms. While these solutions served well in earlier AI eras, the advent of generative AI demands a fundamental shift. The new landscape requires data to be processed in real-time, cleansed, and fresh enough to support dynamic AI applications like conversational agents, recommendation systems, and real-time analytics.
The speaker pointed out that these legacy systems—optimized for batch operations—are insufficient for agentic architectures where immediacy and accuracy are critical. The challenge lies in reinventing how organizations handle data: moving from static, batch-oriented workflows to continuous, intelligent pipelines.