ON-DEMAND WEBINAR
Most data teams know their pipelines need to evolve and shift away from batch loads that run overnight, manual workflows stitched together over the years, and legacy tooling that was never designed for the demands of real-time analytics or AI workloads. Present-day systems that incorporate large language models, such as retrieval augmented generation (RAG), need up-to-date information to remain accurate and useful.
The challenge isn't recognizing the problem. It's figuring out where to start, what to prioritize, and how to modernize without turning it into a six-month replatforming project.
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 cover when Change Data Capture (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 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.
You'll learn how to:
Assess which pipelines to modernize first based on actual business impact
Use CDC to move from batch to incremental replication without disrupting production
Deliver data into modern platforms like Snowflake, Databricks, and Fabric
Take an incremental approach that doesn't require ripping out what's already working
Enable AI systems with fresh, accurate information to avoid hallucinations
