SQL on Big Data

Technology, Architecture, and Innovation

Sumit Pal

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English

APress
18 November 2016

SQL Server & MS SQL

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 queries

Interactive Architectures—Understanding how SQL engines are architected to support low latency on large data sets

Streaming Architectures—Understanding how SQL engines are architected to support queries on data in motion using in-memory and lock-free data structures

Operational Architectures—Understanding how SQL engines are architected for transactional and operational systems to support transactions on Big Data platforms

Innovative Architectures—Explore the rapidly evolving newer SQL engines on Big Data with innovative ideas and concepts

Who This Book Is For: Business analysts, BI engineers, developers, data scientists and architects, and quality assurance professionals

By: Sumit Pal
Imprint: APress
Country of Publication: United States
Edition: 1st ed.
Dimensions: Height: 235mm, Width: 155mm, Spine: 10mm
Weight: 2.818kg
ISBN: 9781484222461
ISBN 10: 1484222466
Pages: 157
Publication Date: 18 November 2016
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.

Sumit Pal is a big data and data science consultant working with multiple clients and advising them on their data architectures and big data solutions as well as providing hands on coding with Spark, Scala, Java and Python. He is a big data, visualization and data science consultant, and a software architect and big data enthusiast and builds end-to-end data-driven analytic systems. He has more than 22 years of experience in the software industry in various roles spanning companies from startups to enterprises. Sumit has worked for Microsoft (SQL server development team), Oracle (OLAP development team) and Verizon (big data analytics team) in a career spanning 22 years. He has extensive experience in building scalable systems across the stack from middle-tier, data tier to visualization for analytics applications, using big data, and NoSQL DB. Sumit has deep expertise in database Internals, data warehouses, dimensional modeling, data science with Java and Python, and SQL. Sumit has MS and BS in Computer Science.

Reviews for SQL on Big Data: Technology, Architecture, and Innovation

“SQL on big data is a well-designed, nicely written book readable by both the technologist and the business decision maker. … From a rapidly expanding field, the author has done a good job at introducing a sample of relevant technologies. … the book is a good introduction to the topic.” (Computing Reviews, May, 2017)