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    Real-Time Big Data Analytics
    Real-Time Big Data Analytics
    Real-Time Big Data Analytics
    Ebook644 pages6 hours

    Real-Time Big Data Analytics

    By Shilpi and Gupta Sumit

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    About this ebook

    Design, process, and analyze large sets of complex data in real time

    About This Book

    - Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using Storm
    - Implement strategies to solve the challenges of real-time data processing
    - Load datasets, build queries, and make recommendations using Spark SQL

    Who This Book Is For

    If you are a Big Data architect, developer, or a programmer who wants to develop applications/frameworks to implement real-time analytics using open source technologies, then this book is for you.

    What You Will Learn

    - Explore big data technologies and frameworks
    - Work through practical challenges and use cases of real-time analytics versus batch analytics
    - Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm
    - Handle and process real-time transactional data
    - Optimize and tune Apache Storm for varied workloads and production deployments
    - Process and stream data with Amazon Kinesis and Elastic MapReduce
    - Perform interactive and exploratory data analytics using Spark SQL
    - Develop common enterprise architectures/applications for real-time and batch analytics

    In Detail

    Enterprise has been striving hard to deal with the challenges of data arriving in real time or near real time.
    Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases.
    From the beginning of the book, we will cover the basics of varied real-time data processing frameworks and technologies. We will discuss and explain the differences between batch and real-time processing in detail, and will also explore the techniques and programming concepts using Apache Storm.
    Moving on, we’ll familiarize you with “Amazon Kinesis” for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program.
    You will learn how to transform your data, get an output from transformations, and persist your results using Spark RDDs, using an interface called Spark SQL to work with Spark.
    At the end of this book, we will introduce Spark Streaming, the streaming library of Spark, and will walk you through the emerging Lambda Architecture (LA), which provides a hybrid platform for big data processing by combining real-time and precomputed batch data to provide a near real-time view of incoming data.

    Style and approach

    This step-by-step is an easy-to-follow, detailed tutorial, filled with practical examples of basic and advanced features.
    Each topic is explained sequentially and supported by real-world examples and executable code snippets.
    LanguageEnglish
    PublisherPackt Publishing
    Release dateFeb 26, 2016
    ISBN9781784397401
    Real-Time Big Data Analytics

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      Book preview

      Real-Time Big Data Analytics - Shilpi

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      Chapter 1. Introducing the Big Data Technology Landscape and Analytics Platform

      The Big Data paradigm has emerged as one of the most powerful in next-generation data storage, management, and analytics. IT powerhouses have actually embraced the change and have accepted that it's here to stay.

      What arrived just as Hadoop, a storage and distributed processing platform, has really graduated and evolved. Today, we have whole panorama of various tools and technologies that specialize in various specific verticals of the Big Data space.

      In this chapter, you will become acquainted with the technology landscape of Big Data and analytics platforms. We will start by introducing the user to the infrastructure, the processing components, and the advent of Big Data. We will also discuss the needs and use cases for near real-time analysis.

      This chapter will cover the following points that will help you to understand the Big Data technology landscape:

      Infrastructure of Big Data

      Components of the Big Data ecosystem

      Analytics architecture

      Distributed batch processing

      Distributed databases (NoSQL)

      Real-time and stream processing

      Big Data – a phenomenon

      The phrase Big Data is not just a new buzzword, it's something that arrived slowly and captured the entire arena. The arrival of Hadoop and its alliance marked the end of the age for the long undefeated reign of traditional databases and warehouses.

      Today, we have a humongous amount of data all around us, in each and every sector of society and the economy; talk about any industry, it's sitting and generating loads of data—for instance, manufacturing, automobiles, finance, the energy sector, consumers, transportation, security, IT, and networks. The advent of Big Data as a field/domain/concept/theory/idea has made it possible to store, process, and analyze these large pools of data to get intelligent insight, and perform informed and calculated decisions. These decisions are driving the recommendations, growth, planning, and projections in all segments of the economy and that's why Big Data has taken the world by storm.

      If we look at the trends in the IT industry, there was an era when people were moving from manual computation to automated, computerized applications, then we ran into an era of enterprise level applications. This era gave birth to architectural flavors such as SAAS and PaaS. Now, we are into an era where we have a huge amount of data, which can be processed and analyzed in cost-effective ways. The world is moving towards open source to get the benefits of reduced license fees, data storage, and computation costs. It has really made it lucrative and affordable for all sectors and segments to harness the power of data. This is making Big Data synonymous with low cost, scalable, highly available, and reliable solutions that can churn huge amounts of data at incredible speed and generate intelligent

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