Data Ingestion for the Connected World

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In this paper, we argue that in many “Big Data” applications, getting data into the system correctly and at scale via traditional ETL (Extract, Transform, and Load) processes is a fundamental roadblock to being able to perform timely analytics or make real-time decisions. The best way to address this problem is to build a new architecture for ETL which takes advantage of the push-based nature of a stream processing system. We discuss the requirements for a streaming ETL engine and describe a generic architecture which satisfies those requirements. We also describe our implementation of streaming ETL using a scalable messaging system (Apache Kafka), a transactional stream processing system (S-Store), and a distributed polystore (Intel’s BigDAWG), as well as propose a new time-series database optimized to handle ingestion internally...