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Real-Time Big Data Analytics
Real-Time Big Data Analytics

Real-Time Big Data Analytics: Design, process, and analyze large sets of complex data in real time

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Profile Icon Shilpi Saxena
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (2 Ratings)
Paperback Feb 2016 326 pages 1st Edition
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Arrow left icon
Profile Icon Shilpi Saxena
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Paperback Feb 2016 326 pages 1st Edition
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Real-Time Big Data Analytics

Chapter 2. Getting Acquainted with Storm

The focus of this chapter is to acquaint readers with Storm, and explain the inception and the journey Storm has been through. It aims to make the user aware of the basic concepts and architecture of Apache Storm and explain with use cases how Storm can be used in real time Big Data analytics.

We will cover the following topics in this chapter:

  • An overview of Storm
  • The journey of Storm
  • Storm abstractions
  • Storm architecture and its components
  • How and when to use Storm
  • Storm internals

An overview of Storm

If someone would ask me to describe Storm in a line, I would use the well-known statement, "Storm is actually Hadoop of real time." Hadoop provides the solution to the volume dimension of Big Data, but it's essentially a batch processing platform. It doesn't come with speed and immediate results/analysis. Though Hadoop has been a turning point for the data storage and computation regime, it cannot be a solution to a problem requiring real-time analysis and results.

Storm addresses the velocity aspect of Big Data. This framework provides the capability to execute distributed computations at lightning fast speed in real-time streaming data. It's a widely used solution to provide real-time alerts and analytics over high velocity streaming data. Storm is a project that's now adopted by Apache. It is proven and known for its capabilities; being under the Apache canopy, it is free and open source. It is a distributed compute engine, which is...

Storm architecture and its components

We have discussed enough about the history and theory of abstractions of Storm; it's now time to dive in and see the framework in execution and get hands on to the real code to actually see Storm in action. We are just one step away from the action part. Before we get there, let's understand what are the various components that get to play in Storm and what is their contribution in the building and orchestration of this framework.

Storm execution can be done in two flavors:

  • Local mode: This is a single node and a nondistributed setup that is generally used for demo and testing. Here, the entire topology is executed in a single worker and thus a single JVM.
  • Distributed mode: This is a multinode setup that is fully or partially distributed and this is the recommended mode for real-time application development and deployment.

The instructions can be referred to from the Apache Storm site at https://storm.apache.org/documentation/Setting-up-a-Storm...

How and when to use Storm

I am a believer of the fact that the quickest way to get acquainted to a tool or technology is to do it, and we have been doing a lot of theoretical talking so far rather than actually doing it, so let's actually begin the fun. We would start with the basic word count topology, I have lot of experience of using Storm on Linux, and there is a lot of online material available for the same. I have used a Windows VM for execution of the word count topology. Here are a couple of prerequisites:

  • apache-storm-0.9.5-src.
  • JDK 1.6+.
  • Python 2.x. (I figured this out by a little trial and error. My Ubuntu always had Python and it never gave any trouble; for example, the word count uses a Python script for splitting sentences, so I set up Python 3 (the latest version), but later figured out that the compatible one is 2.x.)
  • Maven.
  • Eclipse.

Here we go.

Set up the following environment variables accurately:

  • JAVA_HOME
  • MVN_HOME
  • PATH

The PATH variable should have the path to Python installation...

Storm internals

The moment people start talking about Storm, a few key aspects of this framework stand apart:

  • Storm parallelism
  • Storm internal message processing

Now, let's pick each of these attributes and figure out how Storm is able to deliver these capabilities.

Storm parallelism

If we want to enlist the processes that thrive within a Storm cluster, the following are key components to be tracked:

  • Worker process: These are the processes executing on the supervisor node and process a subset of the topology. Each worker process executes in its own JVM. The number of workers allocated to a topology can be specified in the topology builder template and is applicable at the time of topology submission.
  • Executors: These are the threads that are spawned within the worker processes for execution of a bolt or spout. Each executor can run multiple tasks, but being a single thread, these tasks on the executor are performed sequentially. The number of executors is specified while wiring in the bolts...

Summary

We covered quite a bit on Storm, its history, and its inception in this chapter. We introduced the components of Storm along with internal implementations of certain key aspects of Storm. We saw actual code and we now expect you to be able to set up Storm (local and cluster) and execute the basic examples as part of a Storm starter.

In the next chapter, we will initiate the users on various input data sources that are used in conjunction with Storm. We will discuss the reliability of Storm and how to persist data into some stable storage from Storm bolts.

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Key benefits

  • 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

Description

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.

Who is this book 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

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Publication date : Feb 26, 2016
Length: 326 pages
Edition : 1st
Language : English
ISBN-13 : 9781784391409
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Apache
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Publication date : Feb 26, 2016
Length: 326 pages
Edition : 1st
Language : English
ISBN-13 : 9781784391409
Vendor :
Apache
Category :
Languages :
Concepts :

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Table of Contents

11 Chapters
1. Introducing the Big Data Technology Landscape and Analytics Platform Chevron down icon Chevron up icon
2. Getting Acquainted with Storm Chevron down icon Chevron up icon
3. Processing Data with Storm Chevron down icon Chevron up icon
4. Introduction to Trident and Optimizing Storm Performance Chevron down icon Chevron up icon
5. Getting Acquainted with Kinesis Chevron down icon Chevron up icon
6. Getting Acquainted with Spark Chevron down icon Chevron up icon
7. Programming with RDDs Chevron down icon Chevron up icon
8. SQL Query Engine for Spark – Spark SQL Chevron down icon Chevron up icon
9. Analysis of Streaming Data Using Spark Streaming Chevron down icon Chevron up icon
10. Introducing Lambda Architecture Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(2 Ratings)
5 star 50%
4 star 50%
3 star 0%
2 star 0%
1 star 0%
Pethuru Raj Mar 07, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
In the increasingly connected world, the number of data-generating sources is consistently on the rise. This trend and the transition have induced many distinct outcomes: the data size is exponentially growing, the data structure, scope, and speed is also evolving fast, etc. There are big, fast, streaming and IoT data emanating from disparate and distributed sources. There is a widespread realization that the data heaps implicitly possess a variety of actionable insights, which is indispensable for deftly and decisively steering any organization in the right direction. Therefore, there is a clarion call for unearthing a bevy of path-breaking techniques and tools for effectively ingesting, processing, and mining the massive volumes of data for squeezing out useful and usable intelligence. The pioneering Hadoop paradigm has brought in the real disruption on big data, which turns out to be the new normal.In this context, the emergence of the highly deliberated and discoursed Hadoop technique is being widely applauded and adopted across. There are multiple Hadoop implementations in the marketplace these days. Both open source and commercial-grade software solutions are spitting out the data-driven insights and enabling insights-driven decisions for institutions, individuals, and innovators to be distinctively different in their deeds, decisions, and deals. Typically there are two key processing types: the batch and the real-time processing. Hadoop is primarily for doing batch processing of big data. However, the recent trends indicate the need for real-time processing of big data. No doubt, there are several challenges associated with the real-time analytics of tremendous amount of poly-structured data. There are value-added and venerable approaches and articulations in the form of platform-centric as well as infrastructure-specific solutions for efficiently tackling this emerging expectation.In this book, the authors have clearly focused on hugely popular Apache Spark and Storm and other associated software solutions in order to expound all that are needed to empower big data architects and consultants, software engineers and developers with the right and relevant knowledge to build, deploy and deliver sophisticated real-time services and applications. This is a well-written book stuffed and sandwiched with a lot of practical examples, code snippets and easy-to-use optimization tips for equipping IT practitioners and professionals to jump into the data analytics domain quickly and easily.
Amazon Verified review Amazon
Sudhir Chawla May 25, 2016
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
It starts right from the very beginning where most Big Data books start from. The 3/5 Vs of Big Data, which is helpful for the beginner but might give a very mechanical feel to who already have been around the buzz a bit because it does not offer something different that you might not find elsewhere.Terminology, definitions, acronyms are explained in a very insipid and monotonous way. It could have been more interesting by not just stating stuff but explaining or giving analogies in a better way.Every subtopic being point-wise does help in maintaining a flow and remembering stuff easily. The structure and the flow of the entire book is very logical and intuitive.After introducing the concepts this book takes on the enterprise implementation of big-data problems/analytics. It focuses on getting the user acquainted with tools like Storm, Spark, Amazon kinesis and other skills required to quickly design, implement and deploy real-time solutions to big-data problems.Over all, this book focus on implementation and not in-depth conceptual paradigms of Big Data. It is not recommended for explorers or deep-divers because it wont give you much understanding but simply helps you in knowing some tools in order to start implementing the solutions. A bit of background is needed in implementation and visualizing the problems before you take up this book. Else one can be lost in the whys and hows of it.However, for a bit experienced developers this is handy to get going with the tools and sample implementation of solutions supported with codebase.
Amazon Verified review Amazon
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