Neha Pawar is a Founding Engineer at StarTree (https://www.startree.ai/), which aims to democratize data for all users by providing real-time, user-facing analytics. Prior to this, she was part of LinkedIn's Data Analytics Infrastructure org for 5 years, working on Apache Pinot & ThirdEye. She is passionate about big data technologies and real-time analytics databases. Neha is an Apache Pinot PMC and Committer. She has made numerous impactful contributions to Apache Pinot, with a focus on realtime streaming integrations and ingestion. She actively fosters the growing Apache Pinot community & loves to evangelize Pinot by making entertaining video tutorials & illustrations. When not sipping Pinot, you can find Neha jamming with her husband, painting or hiking with her dogs.
When you hear "decision maker", it's natural to think, "C-suite", or "executive". But these days, we're all decision-makers. Restaurant owners, bloggers, big box shoppers, diners - we all have important decisions to make and need instant actionable insights. In order to provide these insights to end-users like us, businesses need access to fast, fresh analytics. Providing user-facing, personalized analytics to all end-users in a realtime, scalable and efficient way, is a hard problem. Apache Pinot solves this problem. Pinot is a distributed datastore purpose-built for ultra-low latency analytics even at extremely high throughput (think milliseconds latencies at tens of thousands of queries per second). In this talk, we will discuss user-facing analytics, and understand why this new & emerging category of analytics is so very challenging. We will peek into Apache Pinot's architecture, ingestion techniques, indexes, and other optimizations that help this system deliver blazing-fast real-time analytics. We'll learn about the interesting ways that companies - such as LinkedIn & Uber - added tremendous value to their products, by adopting Pinot to build rich user-facing analytics ecosystems for all their end-users.