SQL on Big Data

Learn various commercial and open source products that perform SQL on Big Data platforms. You will understand the architectures of the various SQL engines being used and how the tools work internally… Lue lisää Learn various commercial and open source products that perform SQL on Big Data platforms. You will understand the architectures of the various SQL engines being used and how the tools work internally in terms of execution, data movement, latency, scalability, performance, and system requirements. This book consolidates in one place solutions to the challenges associated with the requirements of speed, scalability, and the variety of operations needed for data integration and SQL operations. After discussing the history of the how and why of SQL on Big Data, the book provides in-depth insight into the products, architectures, and innovations happening in this rapidly evolving space. SQL on Big Data discusses in detail the innovations happening, the capabilities on the horizon, and how they solve the issues of performance and scalability and the ability to handle different data types. The book covers how SQL on Big Data engines are permeating the OLTP, OLAP, and Operational analytics space and the rapidly evolving HTAP systems. You will learn the details of:Batch Architectures—Understand the internals and how the existing Hive engine is built and how it is evolving continually to support new features and provide lower latency on queriesInteractive Architectures—Understanding how SQL engines are architected to support low latency on large data setsStreaming Architectures—Understanding how SQL engines are architected to support queries on data in motion using in-memory and lock-free data structuresOperational Architectures—Understanding how SQL engines are architected for transactional and operational systems to support transactions on Big Data platformsInnovative Architectures—Explore the rapidly evolving newer SQL engines on Big Data with innovative ideas and conceptsWho This Book Is For:Business analysts, BI engineers, developers, data scientists and architects, and quality assurance professionals