Enterprise data leaders face an aggressive mandate: deploy generative AI and autonomous agents that drive real operational efficiency. Yet, as teams move past isolated pilots into production, they run into a hard architectural barrier. That barrier? Data gravity.

For years, enterprises invested heavily in decoupled analytics stacks—separating underlying storage from complex, third-party compute clusters. While this approach offered initial flexibility, it has introduced a steep operational debt that must be paid. Highly skilled data engineers spend their working hours sizing compute clusters, managing idle capacity, and building fragile pipelines to shuffle data across security boundaries. Autonomous AI agents cannot reason effectively over stale batch pipelines, and moving massive datasets to external processing layers inflates cloud spend while compromising data governance.

Drawing from fresh enterprise survey data and real-world customer migration benchmarks, this session breaks down:

The hidden financial and human toll of running decoupled compute layers over static storage, from network egress fees to engineering burnout.

Why the agentic era requires in situ processing, executing analytical queries, vector search, and model reasoning directly where data natively lives.

Practical architectural patterns for modernizing Apache Spark workloads, embracing open formats like Apache Iceberg, and leveraging AI-assisted tooling to compress migration timelines from years to weeks.