SQL on Big Data [electronic resource] : Technology, Architecture, and Innovation /
Pal, Sumit.author.
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—an understanding of 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 queries Interactive Architectures—an understanding of how SQL engines are architected to support low latency on large data sets Streaming Architectures—an understanding of how SQL engines are architected to support queries on data in motion using in-memory and lock-free data structures Operational Architectures—an understanding of how SQL engines are architected for transactional and operational systems to support transactions on Big Data platforms Innovative Architectures—an exploration of the rapidly evolving newer SQL engines on Big Data with innovative ideas and concepts.
Includes index.
Chapter 1: Introduction—Why SQL on Big Data/Hadoop? -- Chapter 2: SQL on Big Data/Hadoop—Challenges and Solutions -- Chapter 3: Architectures – Batch.-Chapter 4: Architectures – Interactive -- Chapter 5: Architectures – Streaming -- Chapter 6: Innovations -- Chapter 7: Appendix.
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—an understanding of 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 queries Interactive Architectures—an understanding of how SQL engines are architected to support low latency on large data sets Streaming Architectures—an understanding of how SQL engines are architected to support queries on data in motion using in-memory and lock-free data structures Operational Architectures—an understanding of how SQL engines are architected for transactional and operational systems to support transactions on Big Data platforms Innovative Architectures—an exploration of the rapidly evolving newer SQL engines on Big Data with innovative ideas and concepts.
Big data.
Database management.
Data structures (Computer science).
Computer organization.
Big Data.
Database Management.
Data Structures.
Computer Systems Organization and Communication Networks.