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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
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
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 approachThis 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.
- Sales Rank: #2886752 in Books
- Published on: 2016-02-26
- Released on: 2016-02-26
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .74" w x 7.50" l, 1.24 pounds
- Binding: Paperback
- 326 pages
About the Author
Sumit Gupta
Sumit Gupta is a seasoned professional, innovator, and technology evangelist with over 100 man months of experience in architecting, managing, and delivering enterprise solutions revolving around a variety of business domains, such as hospitality, healthcare, risk management, insurance, and so on. He is passionate about technology and overall he has 15 years of hands-on experience in the software industry and has been using Big Data and cloud technologies over the past 4 to 5 years to solve complex business problems. Sumit has also authored Neo4j Essentials (https://www.packtpub.com/big-dataand-business-intelligence/neo4j-essentials), Building Web Applications with Python and Neo4j (https://www.packtpub.com/application-development/building-web-applications-python-and-neo4j), and Learning Real-time Processing with Spark Streaming (https://www.packtpub.com/big-data-andbusiness-intelligence/learning-real-time-processing-spark-streaming), all with Packt Publishing.
Shilpi
Shilpi Saxena is an IT professional and also a technology evangelist. She is an engineer who has had exposure to various domains (machine to machine space, healthcare, telecom, hiring, and manufacturing). She has experience in all the aspects of conception and execution of enterprise solutions. She has been architecting, managing, and delivering solutions in the Big Data space for the last 3 years; she also handles a high-performance and geographically-distributed team of elite engineers. Shilpi has more than 12 years (3 years in the Big Data space) of experience in the development and execution of various facets of enterprise solutions both in the products and services dimensions of the software industry. An engineer by degree and profession, she has worn varied hats, such as developer, technical leader, product owner, tech manager, and so on, and she has seen all the flavors that the industry has to offer. She has architected and worked through some of the pioneers' production implementations in Big Data on Storm and Impala with autoscaling in AWS. Shilpi has also authored Real-time Analytics with Storm and Cassandra (https://www.packtpub.com/big-data-and-business-intelligence/learning-real-time-analytics-storm-and-cassandra) with Packt Publishing.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Good book for experienced developers
By Sudhir Chawla
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.
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