Real-time big data analytics :: design, process, and analyze large sets of complex data in real time /
Annotation
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing,
2016.
|
| Series: | Community experience distilled.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | Annotation |
| Item Description: | Includes index. |
| Physical Description: | 1 online resource : illustrations. |
| ISBN: | 9781784397401 1784397407 |
Staff View
MARC
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| 520 | 8 | |a Annotation |b Design, process, and analyze large sets of complex data in real timeAbout 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 SQLWho This Book Is ForIf 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 analyticsIn DetailEnterprise 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. | |
| 505 | 0 | |a Introducing the Big Data Technology Landscape and Analytics Platform -- Getting Acquainted with Storm -- Processing Data with Storm -- Introduction to Trident and Optimizing Storm Performance -- Getting Acquainted with Kinesis -- Getting Acquainted with Spark -- Programming with RDDs -- SQL Query Engine for Spark -- Spark SQL -- Analysis of Streaming Data Using Spark Streaming -- Introducing Lambda Architecture. | |
| 650 | 0 | |a Big data. |0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 2 | |a Data Mining |0 https://id.nlm.nih.gov/mesh/D057225 | |
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| author | Gupta, Sumit Saxena, Shilpi |
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| contents | Introducing the Big Data Technology Landscape and Analytics Platform -- Getting Acquainted with Storm -- Processing Data with Storm -- Introduction to Trident and Optimizing Storm Performance -- Getting Acquainted with Kinesis -- Getting Acquainted with Spark -- Programming with RDDs -- SQL Query Engine for Spark -- Spark SQL -- Analysis of Streaming Data Using Spark Streaming -- Introducing Lambda Architecture. |
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| series | Community experience distilled. |
| series2 | Community experience distilled |
| spelling | Gupta, Sumit, author. https://id.oclc.org/worldcat/entity/E39PCjqTXBypTMMpMKC7QKjqDC http://id.loc.gov/authorities/names/n2004007266 Real-time big data analytics : design, process, and analyze large sets of complex data in real time / Sumit Gupta, Shilpi Saxena. Birmingham : Packt Publishing, 2016. 1 online resource : illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Community experience distilled Online resource; title from PDF title page (EBSCO, viewed April 20, 2016) Includes index. Annotation Design, process, and analyze large sets of complex data in real timeAbout 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 SQLWho This Book Is ForIf 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 analyticsIn DetailEnterprise 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. Introducing the Big Data Technology Landscape and Analytics Platform -- Getting Acquainted with Storm -- Processing Data with Storm -- Introduction to Trident and Optimizing Storm Performance -- Getting Acquainted with Kinesis -- Getting Acquainted with Spark -- Programming with RDDs -- SQL Query Engine for Spark -- Spark SQL -- Analysis of Streaming Data Using Spark Streaming -- Introducing Lambda Architecture. Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Data Mining https://id.nlm.nih.gov/mesh/D057225 Données volumineuses. Exploration de données (Informatique) COMPUTERS / Databases / Data Mining bisacsh Data Processing. bisacsh/2013 COMPUTERS. bisacsh/2013 Databases. bisacsh/2013 Distributed Systems & Computing. bisacsh/2013 Systems Architecture. bisacsh/2013 Big data fast Data mining fast Electronic books. Saxena, Shilpi, author. has work: Real-time big data analytics (Text) https://id.oclc.org/worldcat/entity/E39PCGRXpvCcddCtxb8bQ8CjP3 https://id.oclc.org/worldcat/ontology/hasWork 1-78439-140-9 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 |
| spellingShingle | Gupta, Sumit Saxena, Shilpi Real-time big data analytics : design, process, and analyze large sets of complex data in real time / Community experience distilled. Introducing the Big Data Technology Landscape and Analytics Platform -- Getting Acquainted with Storm -- Processing Data with Storm -- Introduction to Trident and Optimizing Storm Performance -- Getting Acquainted with Kinesis -- Getting Acquainted with Spark -- Programming with RDDs -- SQL Query Engine for Spark -- Spark SQL -- Analysis of Streaming Data Using Spark Streaming -- Introducing Lambda Architecture. Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Data Mining https://id.nlm.nih.gov/mesh/D057225 Données volumineuses. Exploration de données (Informatique) COMPUTERS / Databases / Data Mining bisacsh Data Processing. bisacsh/2013 COMPUTERS. bisacsh/2013 Databases. bisacsh/2013 Distributed Systems & Computing. bisacsh/2013 Systems Architecture. bisacsh/2013 Big data fast Data mining fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2012003227 http://id.loc.gov/authorities/subjects/sh97002073 https://id.nlm.nih.gov/mesh/D057225 |
| title | Real-time big data analytics : design, process, and analyze large sets of complex data in real time / |
| title_auth | Real-time big data analytics : design, process, and analyze large sets of complex data in real time / |
| title_exact_search | Real-time big data analytics : design, process, and analyze large sets of complex data in real time / |
| title_full | Real-time big data analytics : design, process, and analyze large sets of complex data in real time / Sumit Gupta, Shilpi Saxena. |
| title_fullStr | Real-time big data analytics : design, process, and analyze large sets of complex data in real time / Sumit Gupta, Shilpi Saxena. |
| title_full_unstemmed | Real-time big data analytics : design, process, and analyze large sets of complex data in real time / Sumit Gupta, Shilpi Saxena. |
| title_short | Real-time big data analytics : |
| title_sort | real time big data analytics design process and analyze large sets of complex data in real time |
| title_sub | design, process, and analyze large sets of complex data in real time / |
| topic | Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Data Mining https://id.nlm.nih.gov/mesh/D057225 Données volumineuses. Exploration de données (Informatique) COMPUTERS / Databases / Data Mining bisacsh Data Processing. bisacsh/2013 COMPUTERS. bisacsh/2013 Databases. bisacsh/2013 Distributed Systems & Computing. bisacsh/2013 Systems Architecture. bisacsh/2013 Big data fast Data mining fast |
| topic_facet | Big data. Data mining. Data Mining Données volumineuses. Exploration de données (Informatique) COMPUTERS / Databases / Data Mining Data Processing. COMPUTERS. Databases. Distributed Systems & Computing. Systems Architecture. Big data Data mining Electronic books. |
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