Power Your Delta Lake with Streaming Transactional Changes

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Organizations are adopting data digitization and data-driven decision making is at the heart of this transformation. Cloud Data Lakes and Datawarehouses provide great flexibility to proto-type and roll out applications continuously at much lower costs.

Transactional databases are optimized for processing huge volumes of transactions in real-time, whereas the cloud data lake needs to be optimized for analyzing huge volumes of data quickly. This brings about a challenge in creating a streamlined data flow process from capturing realtime transactions into a cloud datawarehouse to drive realtime insights in a scalable and cost effective manner.

In this session, we’ll show how organizations can easily overcome that challenge by adopting a robust platform with StreamSets and Delta Lake. StreamSets provides a no-code framework to automate ingestion of transactional data and data processing on Spark, while Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.

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About Rupal Shah


Rupal is the Director of Cloud Services at StreamSets and extremely passionate about technology. Rupal has been working at StreamSets over 3 years, onboarding customers and driving the platform with integration of eco-systems. She works very closely with technology partners and drives the StreamSets stack to be best in class with the big data ecosystems.