From Batch to Real-Time: What It Actually Takes to Modernize Your Data Pipelines

Most data teams know their pipelines need to evolve. Batch loads that run overnight, manual workflows stitched together over the years, legacy tooling that was never designed for the demands of real-time analytics or the AI agents that are about to depend on them. But the challenge is figuring out where to start, what to prioritize, and how to modernize without turning it into a six-month replatforming project.

The stakes are higher than they used to be. Agentic RAG systems retrieve and reason over live enterprise data and they're only as reliable as the pipelines feeding them. Stale batch data, inconsistent schemas, and siloed sources don't just slow down your analysts. They cause agents to retrieve the wrong context and fail in production.

In this session, Kim Fessel joins Jess Ramos of Big Data Energy and Manish Patel, GM of Data Integration at CData, to talk through what pipeline modernization actually looks like in practice. We'll cover when CDC is the right move versus when it's overkill, how to approach hybrid environments where legacy and cloud systems need to coexist, and what separates teams that modernize incrementally from those that get stuck in planning mode.

We'll also walk through how CData Sync fits into this, from CDC across sources like SQL Server and Oracle, to pipeline orchestration and delivery into open table formats like Delta Lake and Iceberg, the same formats underpinning retrieval in modern agentic RAG architectures.

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