Providing true interactive and scalable BI on Hadoop is proven to be one of the biggest challenges that is preventing completion of legacy EDW OLAP system transit to Hadoop. While we have all seen many benchmarks running consecutive queries claiming success, having thousands of concurrent business users sending complicated generated queries from their dashboards over billions of records while delivering interactive speed is yet to be seen.
In this session we will discuss how an architecture that replaces full-scan brute-force approach with adaptive indexing and auto-generated cubes can dramatically reduce the resources and effort per query, resulting in interactive performance for high concurrency workloads and explain how this is achieved with minimum data engineering efforts. We will also discuss how this architecture can be seamlessly integrated with Hive to provide a complete OLAP-on-Hadoop solution.
Session will include live demo of complex business dashboards connected to Hive and accessing billions of rows at interactive speed.