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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: