Chinmay Soman is a founding engineer in StarTree, building real time analytics solutions at scale. Previously he led the streaming platform team at Uber for building a large scale, self-serve platform around messaging, stream processing and OLAP technologies. Before that, he worked at LinkedIn and IBM, focussing on distributed systems and security. He’s a PMC member of Apache Samza and a committer on Apache Pinot, Voldemort, uReplicator and AthenaX.
Real-time analytics has transformed the way companies do business. It has unlocked the ability to make real-time decisions such as customer incentives, business metrics, fraud detection and provide a personalized user experience that accelerates growth and user retention. This is a complex problem and naturally, there are several OLAP (OnLine Analytics Processing) solutions out there, each focusing on a different aspect.
In order to support all such use cases, we need an ideal OLAP platform that has the ability to support extremely high query throughput with low latency and at the same time provide high query accuracy – in the presence of data duplication and real-time updates. In addition, the same system must be able to ingest data from all kinds of data sources, handle unstructured data and real-time upserts. While there are different ways of solving each such problem scenario, ideally we want one unified platform that can be easily customized. In this talk, we will go over the rich capabilities of Apache Pinot that make it an ideal OLAP platform.