Enabling Real-Time Business with Change Data Capture


Machine learning (ML) and artificial intelligence (AI) enable intelligent processes that can autonomously make decisions in real-time. The real challenge for effective ML and AI is getting all relevant data to a converged data platform in real-time, where it can be processed using modern technologies and integrated into any downstream systems.

Running a business in real-time means being able to react to important business events as they happen. Applications that support day-to-day operations, however, are often scattered across the organization making it difficult to enable real-time movement of data.

In this session, MapR and StreamSets discussed how change data capture (CDC) can be used to enable real-time workloads to drive success with ML and AI. You’ll see demonstrations of technologies that enable CDC, and specifically learn how to:

  • Utilize change data capture (CDC) for efficient real-time data movement & processing
  • Connect your databases, data warehouses, and data lakes without code
  • Use MapR-DB as both source and destination for change data capture


Audrey Egan
Rupal Shah
Solution Engineer
Solution Engineer
MapR Technologies

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