Real-Time Big Data Analytics

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

Sumit Gupta, Shilpi

2 customer reviews
Design, process, and analyze large sets of complex data in real time
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Book Details

ISBN 139781784391409
Paperback326 pages

Book 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.

Table of Contents

Chapter 1: Introducing the Big Data Technology Landscape and Analytics Platform
Big Data – a phenomenon
The Big Data dimensional paradigm
The Big Data ecosystem
The Big Data infrastructure
Components of the Big Data ecosystem
Distributed batch processing
Distributed databases (NoSQL)
Real-time processing
Summary
Chapter 2: Getting Acquainted with Storm
An overview of Storm
Storm architecture and its components
How and when to use Storm
Storm internals
Summary
Chapter 3: Processing Data with Storm
Storm input sources
Other sources for input to Storm
Reliability of data processing
Storm simple patterns
Storm persistence
Summary
Chapter 4: Introduction to Trident and Optimizing Storm Performance
Working with Trident
Understanding LMAX
Storm internode communication
Understanding the Storm UI
Optimizing Storm performance
Summary
Chapter 5: Getting Acquainted with Kinesis
Architectural overview of Kinesis
Creating a Kinesis streaming service
Summary
Chapter 6: Getting Acquainted with Spark
An overview of Spark
The architecture of Spark
Resilient distributed datasets (RDD)
Writing and executing our first Spark program
Summary
Chapter 7: Programming with RDDs
Understanding Spark transformations and actions
Programming Spark transformations and actions
Handling persistence in Spark
Summary
Chapter 8: SQL Query Engine for Spark – Spark SQL
The architecture of Spark SQL
Coding our first Spark SQL job
Converting RDDs to DataFrames
Working with Parquet
Working with Hive tables
Performance tuning and best practices
Summary
Chapter 9: Analysis of Streaming Data Using Spark Streaming
High-level architecture
Coding our first Spark Streaming job
Querying streaming data in real time
Deployment and monitoring
Summary
Chapter 10: Introducing Lambda Architecture
What is Lambda Architecture
The technology matrix for Lambda Architecture
Realization of Lambda Architecture
Summary

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

Authors

Table of Contents

Chapter 1: Introducing the Big Data Technology Landscape and Analytics Platform
Big Data – a phenomenon
The Big Data dimensional paradigm
The Big Data ecosystem
The Big Data infrastructure
Components of the Big Data ecosystem
Distributed batch processing
Distributed databases (NoSQL)
Real-time processing
Summary
Chapter 2: Getting Acquainted with Storm
An overview of Storm
Storm architecture and its components
How and when to use Storm
Storm internals
Summary
Chapter 3: Processing Data with Storm
Storm input sources
Other sources for input to Storm
Reliability of data processing
Storm simple patterns
Storm persistence
Summary
Chapter 4: Introduction to Trident and Optimizing Storm Performance
Working with Trident
Understanding LMAX
Storm internode communication
Understanding the Storm UI
Optimizing Storm performance
Summary
Chapter 5: Getting Acquainted with Kinesis
Architectural overview of Kinesis
Creating a Kinesis streaming service
Summary
Chapter 6: Getting Acquainted with Spark
An overview of Spark
The architecture of Spark
Resilient distributed datasets (RDD)
Writing and executing our first Spark program
Summary
Chapter 7: Programming with RDDs
Understanding Spark transformations and actions
Programming Spark transformations and actions
Handling persistence in Spark
Summary
Chapter 8: SQL Query Engine for Spark – Spark SQL
The architecture of Spark SQL
Coding our first Spark SQL job
Converting RDDs to DataFrames
Working with Parquet
Working with Hive tables
Performance tuning and best practices
Summary
Chapter 9: Analysis of Streaming Data Using Spark Streaming
High-level architecture
Coding our first Spark Streaming job
Querying streaming data in real time
Deployment and monitoring
Summary
Chapter 10: Introducing Lambda Architecture
What is Lambda Architecture
The technology matrix for Lambda Architecture
Realization of Lambda Architecture
Summary

Book Details

ISBN 139781784391409
Paperback326 pages
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