This book explains various commercial and open source products that perform SQL on Hadoop and big data. It equips the reader with an understanding of the various architectures being used and how the tools work internally in terms of execution, data movement, latency, scalability, performance, and system requirements. SQL on Big Data consolidates in one place solutions to the challenges associated with the requirements of speed, scalability, and variety of operations needed for data integration and SQL operations. After discussing the history of how and why SQL on Hadoop and big data provides the best way to look at data, the book offers an understanding of the direction of this rapidly evolving space. SQL on Big Data lays out the picture of the road ahead-what capabilities are on the horizon and how do they solve the issues of performance and scalability and the ability to handle different data types? The book covers how SQL on big data is permeating the OLTP, OLAP, and operational analytics space and rapidly evolving HTAP systems. The book contains detailed coverage of: Batch architectures-an understanding of the internals and how the existing Hive engine is evolving. Interactive architectures-an understanding of how systems are architected and how they solve problems using MPP and indexes. Streaming architectures-understanding of how the systems are constructed and how they solve problems using in-memory and lock-free data structures.
