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Big Data Analytics.
A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clustersAbout This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing
2016.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 520 | |a A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clustersAbout This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components - Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components - HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data. Style and approach This step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-life examples. Practical tutorial explains data science in simple terms to help programmers and data analysts get started with Data Science. | ||
| 588 | 0 | |a Vendor-supplied metadata. | |
| 505 | 0 | |a Cover; Copyright; Credits; About the Author; Acknowledgement; About the Reviewers; www.PacktPub.com; Preface; Chapter 1: Big Data Analytics at a 10,000-Foot View; Big Data analytics and the role of Hadoop and Spark; A typical Big Data analytics project life cycle; Identifying the problem and outcomes; Identifying the necessary data; Data collection; Preprocessing data and ETL; Performing analytics; Visualizing data; The role of Hadoop and Spark; Big Data science and the role of Hadoop and Spark; A fundamental shift from data analytics to data science; Data scientists versus software engineers. | |
| 505 | 8 | |a Data scientists versus data analystsData scientists versus business analysts; A typical data science project life cycle; Hypothesis and modeling; Measuring the effectiveness; Making improvements; Communicating the results; The role of Hadoop and Spark; Tools and techniques; Real-life use cases; Summary; Chapter 2: Getting Started with Apache Hadoop and Apache Spark; Introducing Apache Hadoop; Hadoop Distributed File System; Features of HDFS; MapReduce; MapReduce features; MapReduce v1 versus MapReduce v2; MapReduce v1 challenges; YARN; Storage options on Hadoop; File formats. | |
| 505 | 8 | |a Compression formatsIntroducing Apache Spark; Spark history; What is Apache Spark?; What Apache Spark is not; MapReduce issues; Spark's stack; Why Hadoop plus Spark?; Hadoop features; Spark features; Frequently asked questions about Spark; Installing Hadoop plus Spark clusters; Summary; Chapter 3: Deep Dive into Apache Spark; Starting Spark daemons; Working with CDH; Working with HDP, MapR, and Spark pre-built packages; Learning Spark core concepts; Ways to work with Spark; Spark Shell; Spark applications; Resilient Distributed Dataset; Method 1 -- parallelizing a collection. | |
| 505 | 8 | |a Method 2 -- reading from a fileSpark context; Transformations and actions; Parallelism in RDDs; Lazy evaluation; Lineage Graph; Serialization; Leveraging Hadoop file formats in Spark; Data locality; Shared variables; Pair RDDs; Lifecycle of Spark program; Pipelining; Spark execution summary; Spark applications; Spark Shell versus Spark applications; Creating a Spark context; SparkConf; SparkSubmit; Spark Conf precedence order; Important application configurations; Persistence and caching; Storage levels; What level to choose?; Spark resource managers -- Standalone, YARN, and Mesos. | |
| 505 | 8 | |a Local versus cluster modeCluster resource managers; Standalone; YARN; Mesos; Which resource manager to use?; Summary; Chapter 4: Big Data Analytics with Spark SQL, DataFrames, and Datasets; History of Spark SQL; Architecture of Spark SQL; Introducing SQL, Datasources, DataFrame, and Dataset APIs; Evolution of DataFrames and Datasets; What's wrong with RDDs?; RDD Transformations versus Dataset and DataFrames Transformations; Why Datasets and DataFrames?; Optimization; Speed; Automatic Schema Discovery; Multiple sources, multiple languages; Interoperability between RDDs and others. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Big data |x Security measures. | |
| 650 | 6 | |a Données volumineuses |x Sécurité |x Mesures. | |
| 655 | 4 | |a Databases; Enterprise Applications. | |
| 776 | 0 | 8 | |i Print version: |a Ankam, Venkat. |t Big Data Analytics. |d Birmingham : Packt Publishing, ©2016 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4699930 |z Texto completo |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL4699930 | ||
| 994 | |a 92 |b IZTAP | ||