Shilpi Saxena – författare

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 StormImplement strategies to solve the challenges of real-time data processingLoad 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 frameworksWork through practical challenges and use cases of real-time analytics versus batch analyticsDevelop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache StormHandle and process real-time transactional dataOptimize and tune Apache Storm for varied workloads and production deploymentsProcess and stream data with Amazon Kinesis and Elastic MapReducePerform interactive and exploratory data analytics using Spark SQLDevelop 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.

Examine the techniques and Java tools supporting the growing field of data scienceKey FeaturesYour entry ticket to the world of data science with the stability and power of JavaExplore, analyse, and visualize your data effectively using easy-to-follow examplesMake your Java applications more capable using machine learningBook Descriptionpara 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutionsWhat you will learnUnderstand the nature and key concepts used in the field of data scienceGrasp how data is collected, cleaned, and processedBecome comfortable with key data analysis techniquesSee specialized analysis techniques centered on machine learningMaster the effective visualization of your dataWork with the Java APIs and techniques used to perform data analysisWho this book is forWith its tutorial approach, this data science book has been written for experienced Java programmers who want to better understand the field of data science and learn how Java supports its underlying techniques. The step-by-step instructional style also makes Java for Data Science ideal for beginners, allowing you to get up and running quickly.