SQL on Big Data

SQL on Big Data
Author: SUMIT PAL
Publsiher: Apress
Total Pages: 157
Release: 2016-12-11
ISBN: 9781484222461
Category: Computers
Language: EN, FR, DE, ES & NL

SQL on Big Data Book Excerpt:

Learn various commercial and open source products that perform SQL on Big Data platforms. You will understand the architectures of the various SQL engines being used and how the tools work internally in terms of execution, data movement, latency, scalability, performance, and system requirements. This book consolidates in one place solutions to the challenges associated with the requirements of speed, scalability, and the variety of operations needed for data integration and SQL operations. After discussing the history of the how and why of SQL on Big Data, the book provides in-depth insight into the products, architectures, and innovations happening in this rapidly evolving space. SQL on Big Data discusses in detail the innovations happening, the capabilities on the horizon, and how they solve the issues of performance and scalability and the ability to handle different data types. The book covers how SQL on Big Data engines are permeating the OLTP, OLAP, and Operational analytics space and the rapidly evolving HTAP systems. You will learn the details of: Batch Architectures—an understanding of the internals and how the existing Hive engine is built and how it is evolving continually to support new features and provide lower latency on queries Interactive Architectures—an understanding of how SQL engines are architected to support low latency on large data sets Streaming Architectures—an understanding of how SQL engines are architected to support queries on data in motion using in-memory and lock-free data structures Operational Architectures—an understanding of how SQL engines are architected for transactional and operational systems to support transactions on Big Data platforms Innovative Architectures—an exploration of the rapidly evolving newer SQL engines on Big Data with innovative ideas and concepts

SQL on Big Data

SQL on Big Data
Author: Sumit Pal
Publsiher: Apress
Total Pages: 165
Release: 2016-11-17
ISBN: 1484222474
Category: Computers
Language: EN, FR, DE, ES & NL

SQL on Big Data Book Excerpt:

Learn various commercial and open source products that perform SQL on Big Data platforms. You will understand the architectures of the various SQL engines being used and how the tools work internally in terms of execution, data movement, latency, scalability, performance, and system requirements. This book consolidates in one place solutions to the challenges associated with the requirements of speed, scalability, and the variety of operations needed for data integration and SQL operations. After discussing the history of the how and why of SQL on Big Data, the book provides in-depth insight into the products, architectures, and innovations happening in this rapidly evolving space. SQL on Big Data discusses in detail the innovations happening, the capabilities on the horizon, and how they solve the issues of performance and scalability and the ability to handle different data types. The book covers how SQL on Big Data engines are permeating the OLTP, OLAP, and Operational analytics space and the rapidly evolving HTAP systems. You will learn the details of: Batch Architectures—Understand the internals and how the existing Hive engine is built and how it is evolving continually to support new features and provide lower latency on queries Interactive Architectures—Understanding how SQL engines are architected to support low latency on large data sets Streaming Architectures—Understanding how SQL engines are architected to support queries on data in motion using in-memory and lock-free data structures Operational Architectures—Understanding how SQL engines are architected for transactional and operational systems to support transactions on Big Data platforms Innovative Architectures—Explore the rapidly evolving newer SQL engines on Big Data with innovative ideas and concepts Who This Book Is For: Business analysts, BI engineers, developers, data scientists and architects, and quality assurance professionals/div

Big Data and Hadoop

Big Data and Hadoop
Author: VK Jain
Publsiher: KHANNA PUBLISHING
Total Pages: 600
Release: 2017-01-01
ISBN: 938260913X
Category: Education
Language: EN, FR, DE, ES & NL

Big Data and Hadoop Book Excerpt:

This book introduces you to the Big Data processing techniques addressing but not limited to various BI (business intelligence) requirements, such as reporting, batch analytics, online analytical processing (OLAP), data mining and Warehousing, and predictive analytics. The book has been written on IBMs Platform of Hadoop framework. IBM Infosphere BigInsight has the highest amount of tutorial matter available free of cost on Internet which makes it easy to acquire proficiency in this technique. This therefore becomes highly vunerable coaching materials in easy to learn steps. The book optimally provides the courseware as per MCA and M. Tech Level Syllabi of most of the Universities. All components of big Data Platform like Jaql, Hive Pig, Sqoop, Flume , Hadoop Streaming, Oozie: HBase, HDFS, FlumeNG, Whirr, Cloudera, Fuse , Zookeeper and Mahout: Machine learning for Hadoop has been discussed in sufficient Detail with hands on Exercises on each.

Big Data 2 0 Processing Systems

Big Data 2 0 Processing Systems
Author: Sherif Sakr
Publsiher: Springer Nature
Total Pages: 145
Release: 2020-07-09
ISBN: 3030441873
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data 2 0 Processing Systems Book Excerpt:

This book provides readers the “big picture” and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Next, Chapter 6 focuses on covering the emerging frameworks and systems in the domain of scalable machine learning and deep learning processing. Lastly, Chapter 7 shares conclusions and an outlook on future research challenges. This new and considerably enlarged second edition not only contains the completely new chapter 6, but also offers a refreshed content for the state-of-the-art in all domains of big data processing over the last years. Overall, the book offers a valuable reference guide for professional, students, and researchers in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.

Big Data Benchmarks Performance Optimization and Emerging Hardware

Big Data Benchmarks  Performance Optimization  and Emerging Hardware
Author: Jianfeng Zhan,Rui Han,Chuliang Weng
Publsiher: Springer
Total Pages: 221
Release: 2014-11-10
ISBN: 3319130218
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Benchmarks Performance Optimization and Emerging Hardware Book Excerpt:

This book constitutes the thoroughly revised selected papers of the 4th and 5th workshops on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 4 and BPOE 5, held respectively in Salt Lake City, in March 2014, and in Hangzhou, in September 2014. The 16 papers presented were carefully reviewed and selected from 30 submissions. Both workshops focus on architecture and system support for big data systems, such as benchmarking; workload characterization; performance optimization and evaluation; emerging hardware.

Big Data Analytics

Big Data Analytics
Author: P. Krishna Reddy,Ashish Sureka,Sharma Chakravarthy,Subhash Bhalla
Publsiher: Springer
Total Pages: 311
Release: 2017-12-04
ISBN: 3319724134
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Analytics Book Excerpt:

This book constitutes the refereed conference proceedings of the 5th International Conference on Big Data Analytics, BDA 2017, held in Hyderabad, India, in December 2017. The 21 revised full papers were carefully reviewed and selected from 80 submissions and cover topics on big data analytics, information and knowledge management, mining of massive datasets, computational modeling, data mining and analysis.

Modern Big Data Architectures

Modern Big Data Architectures
Author: Dominik Ryzko
Publsiher: John Wiley & Sons
Total Pages: 208
Release: 2020-04-09
ISBN: 1119597935
Category: Computers
Language: EN, FR, DE, ES & NL

Modern Big Data Architectures Book Excerpt:

Provides an up-to-date analysis of big data and multi-agent systems The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics. This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detailed solutions suitable for a wide variety of applications. The author, an internationally-recognized expert in Big Data and distributed Artificial Intelligence, demonstrates how base concepts such as agent, actor, and micro-service have reached a point of convergence—enabling next generation systems to be built by incorporating the best aspects of the field. This book: Illustrates how data sets are produced and how they can be utilized in various areas of industry and science Explains how to apply common computational models and state-of-the-art architectures to process Big Data tasks Discusses current and emerging Big Data applications of Artificial Intelligence Modern Big Data Architectures: A Multi-Agent Systems Perspective is a timely and important resource for data science professionals and students involved in Big Data analytics, and machine and artificial learning.

Proceedings of 4th International Conference on BigData Analysis and Data Mining 2017

Proceedings of 4th International Conference on BigData Analysis and Data Mining 2017
Author: ConferenceSeries
Publsiher: ConferenceSeries
Total Pages: 95
Release: 2022
ISBN: 1928374650XXX
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Proceedings of 4th International Conference on BigData Analysis and Data Mining 2017 Book Excerpt:

September 07-08, 2017 Paris, France Key Topics : Cloud computing, Forecasting from Big Data, Optimization and Big Data, New visualization techniques, Social network analysis, Search and data mining, Complexity and Algorithms, Open Data, ETL (Extract, Transform and Load), OLAP Technologies, Big Data Algorithm, Data Mining Analysis, Kernel Methods, Frequent Pattern Mining, Clustering, Data Privacy and Ethics, Big Data Technologies, Business Analytics, Data Mining Methods and Algorithms, Data Mining Tasks and Processes, Data Mining Applications in Science, Engineering, Healthcare and Medicine, Big Data Applications, Data Mining Tools and Software, Data Warehousing, Artificial Intelligence,

Big Data Analytics with Java

Big Data Analytics with Java
Author: Rajat Mehta
Publsiher: Packt Publishing Ltd
Total Pages: 418
Release: 2017-07-31
ISBN: 1787282198
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Analytics with Java Book Excerpt:

Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code.

Big Data Analytics with Spark

Big Data Analytics with Spark
Author: Mohammed Guller
Publsiher: Apress
Total Pages: 290
Release: 2015-12-29
ISBN: 1484209648
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Analytics with Spark Book Excerpt:

Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert. Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding. Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real time data streams and so on. As a result, adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics. This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib. Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program that underlies Spark. You’ll learn the basics of functional programming in Scala, so that you can write Spark applications in it. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, like Hive, Avro, Kafka and so on. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to know is programming in any language. There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost—possibly a big boost—to your career.

Scala and Spark for Big Data Analytics

Scala and Spark for Big Data Analytics
Author: Md. Rezaul Karim,Sridhar Alla
Publsiher: Packt Publishing Ltd
Total Pages: 786
Release: 2017-07-25
ISBN: 1783550503
Category: Computers
Language: EN, FR, DE, ES & NL

Scala and Spark for Big Data Analytics Book Excerpt:

Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts Work on a wide array of applications, from simple batch jobs to stream processing and machine learning Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn Understand object-oriented & functional programming concepts of Scala In-depth understanding of Scala collection APIs Work with RDD and DataFrame to learn Spark's core abstractions Analysing structured and unstructured data using SparkSQL and GraphX Scalable and fault-tolerant streaming application development using Spark structured streaming Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML Build clustering models to cluster a vast amount of data Understand tuning, debugging, and monitoring Spark applications Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big. Style and approach Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist.

Beginning Big Data with Power BI and Excel 2013

Beginning Big Data with Power BI and Excel 2013
Author: Neil Dunlop
Publsiher: Apress
Total Pages: 258
Release: 2015-10-04
ISBN: 1484205294
Category: Computers
Language: EN, FR, DE, ES & NL

Beginning Big Data with Power BI and Excel 2013 Book Excerpt:

In Beginning Big Data with Power BI and Excel 2013, you will learn to solve business problems by tapping the power of Microsoft’s Excel and Power BI to import data from NoSQL and SQL databases and other sources, create relational data models, and analyze business problems through sophisticated dashboards and data-driven maps. While Beginning Big Data with Power BI and Excel 2013 covers prominent tools such as Hadoop and the NoSQL databases, it recognizes that most small and medium-sized businesses don’t have the Big Data processing needs of a Netflix, Target, or Facebook. Instead, it shows how to import data and use the self-service analytics available in Excel with Power BI. As you’ll see through the book’s numerous case examples, these tools—which you already know how to use—can perform many of the same functions as the higher-end Apache tools many people believe are required to carry out in Big Data projects. Through instruction, insight, advice, and case studies, Beginning Big Data with Power BI and Excel 2013 will show you how to: Import and mash up data from web pages, SQL and NoSQL databases, the Azure Marketplace and other sources. Tap into the analytical power of PivotTables and PivotCharts and develop relational data models to track trends and make predictions based on a wide range of data. Understand basic statistics and use Excel with PowerBI to do sophisticated statistical analysis—including identifying trends and correlations. Use SQL within Excel to do sophisticated queries across multiple tables, including NoSQL databases. Create complex formulas to solve real-world business problems using Data Analysis Expressions (DAX).

Cloud Computing and Big Data

Cloud Computing and Big Data
Author: Marcelo Naiouf,Franco Chichizola,Enzo Rucci
Publsiher: Springer
Total Pages: 155
Release: 2019-07-26
ISBN: 3030277135
Category: Computers
Language: EN, FR, DE, ES & NL

Cloud Computing and Big Data Book Excerpt:

This book constitutes the revised selected papers of the 7th International Conference on Cloud Computing and Big Data, JCC&BD 2019, held in La Plata, Buenos Aires, Argentina, in June 2019. The 12 full papers presented were carefully reviewed and selected from a total of 31 submissions. They are dealing with such topics as cloud computing and HPC; Big Data and data intelligence; mobile computing.

Big Data Management and Processing

Big Data Management and Processing
Author: Kuan-Ching Li,Hai Jiang,Albert Y. Zomaya
Publsiher: CRC Press
Total Pages: 469
Release: 2017-05-19
ISBN: 1498768083
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Management and Processing Book Excerpt:

From the Foreword: "Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies." ---Sartaj Sahni, University of Florida, USA "Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields. --Hai Jin, Huazhong University of Science and Technology, China Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems. The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions. The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects.

High Performance Big Data Computing

High Performance Big Data Computing
Author: Dhabaleswar K. Panda,Xiaoyi Lu,Dipti Shankar
Publsiher: MIT Press
Total Pages: 272
Release: 2022-08-02
ISBN: 0262369427
Category: Computers
Language: EN, FR, DE, ES & NL

High Performance Big Data Computing Book Excerpt:

An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.

Big Data Benchmarking

Big Data Benchmarking
Author: Tilmann Rabl,Kai Sachs,Meikel Poess,Chaitanya Baru,Hans-Arno Jacobson
Publsiher: Springer
Total Pages: 157
Release: 2015-06-13
ISBN: 3319202332
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Benchmarking Book Excerpt:

This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Big Data Benchmarking, WBDB 2014, held in Potsdam, Germany, in August 2014. The 13 papers presented in this book were carefully reviewed and selected from numerous submissions and cover topics such as benchmarks specifications and proposals, Hadoop and MapReduce - in the different context such as virtualization and cloud - as well as in-memory, data generation, and graphs.

Hands On Big Data Analytics with PySpark

Hands On Big Data Analytics with PySpark
Author: Rudy Lai,Bartłomiej Potaczek
Publsiher: Packt Publishing Ltd
Total Pages: 182
Release: 2019-03-29
ISBN: 1838648836
Category: Computers
Language: EN, FR, DE, ES & NL

Hands On Big Data Analytics with PySpark Book Excerpt:

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs Key FeaturesWork with large amounts of agile data using distributed datasets and in-memory cachingSource data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3Employ the easy-to-use PySpark API to deploy big data Analytics for productionBook Description Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively. What you will learnGet practical big data experience while working on messy datasetsAnalyze patterns with Spark SQL to improve your business intelligenceUse PySpark's interactive shell to speed up development timeCreate highly concurrent Spark programs by leveraging immutabilityDiscover ways to avoid the most expensive operation in the Spark API: the shuffle operationRe-design your jobs to use reduceByKey instead of groupByCreate robust processing pipelines by testing Apache Spark jobsWho this book is for This book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to magnify the impact of your business, this book is for you.

Big Data Computing

Big Data Computing
Author: Vivek Kale
Publsiher: CRC Press
Total Pages: 495
Release: 2016-11-25
ISBN: 1498715346
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Big Data Computing Book Excerpt:

This book unravels the mystery of Big Data computing and its power to transform business operations. The approach it uses will be helpful to any professional who must present a case for realizing Big Data computing solutions or to those who could be involved in a Big Data computing project. It provides a framework that enables business and technical managers to make optimal decisions necessary for the successful migration to Big Data computing environments and applications within their organizations.

Big Data Analytics

Big Data Analytics
Author: Anirban Mondal,Himanshu Gupta,Jaideep Srivastava,P. Krishna Reddy,D.V.L.N. Somayajulu
Publsiher: Springer
Total Pages: 424
Release: 2018-12-11
ISBN: 3030047806
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Analytics Book Excerpt:

This book constitutes the refereed proceedings of the 6th International Conference on Big Data analytics, BDA 2018, held in Warangal, India, in December 2018. The 29 papers presented in this volume were carefully reviewed and selected from 93 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; financial data analytics and data streams; web and social media data; big data systems and frameworks; predictive analytics in healthcare and agricultural domains; and machine learning and pattern mining.

Advancing Big Data Benchmarks

Advancing Big Data Benchmarks
Author: Tilmann Rabl,Nambiar Raghunath,Meikel Poess,Milind Bhandarkar,Hans-Arno Jacobsen,Chaitanya Baru
Publsiher: Springer
Total Pages: 203
Release: 2014-10-08
ISBN: 3319105965
Category: Computers
Language: EN, FR, DE, ES & NL

Advancing Big Data Benchmarks Book Excerpt:

This book constitutes the thoroughly refereed joint proceedings of the Third and Fourth Workshop on Big Data Benchmarking. The third WBDB was held in Xi'an, China, in July 2013 and the Fourth WBDB was held in San José, CA, USA, in October, 2013. The 15 papers presented in this book were carefully reviewed and selected from 33 presentations. They focus on big data benchmarks; applications and scenarios; tools, systems and surveys.