Wesley Reisz talks to Sid Anand, a data architect at cybersecurity company Agari, about building cloud-native data pipelines. The focus of their discussion is around a solution Agari uses that is built from Amazon Kinesis Streams, serverless functions, and auto scaling groups.

Sid Anand is an architect at Agari, and a former technical architect at eBay, Netflix, and LinkedIn. He has 15 years of data infrastructure experience at scale, is a PMC for Apache Airflow, and is also a program committee chair for QCon San Francisco and QCon London.

Why listen to this podcast

- Real-time data pipeline processing is very latency sensitive

- Micro-batching allows much smaller amounts of data to be processed

- Use the appropriate data store (or stores) to support the use of the dataIngesting data quickly into a clean database with minimal indexes can be fast

- Communicate using a messaging system that supports schema evolution

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