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Big Data Analytics with Microsoft HDInsight in 24 Hours, Sams Teach Yourself

Big Data Analytics with Microsoft HDInsight in 24 Hours, Sams Teach Yourself

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Description

  • Copyright 2016
  • Dimensions: 7" x 9-1/8"
  • Pages: 592
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-403530-5
  • ISBN-13: 978-0-13-403530-7

Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours


In just 24 lessons of one hour or less, Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours helps you leverage Hadoop’s power on a flexible, scalable cloud platform using Microsoft’s newest business intelligence, visualization, and productivity tools.


This book’s straightforward, step-by-step approach shows you how to provision, configure, monitor, and troubleshoot HDInsight and use Hadoop cloud services to solve real analytics problems. You’ll gain more of Hadoop’s benefits, with less complexity–even if you’re completely new to Big Data analytics. Every lesson builds on what you’ve already learned, giving you a rock-solid foundation for real-world success.


Practical, hands-on examples show you how to apply what you learn

Quizzes and exercises help you test your knowledge and stretch your skills

Notes and tips point out shortcuts and solutions

Learn how to…

·         Master core Big Data and NoSQL concepts, value propositions, and use cases

·         Work with key Hadoop features, such as HDFS2 and YARN

·         Quickly install, configure, and monitor Hadoop (HDInsight) clusters in the cloud

·         Automate provisioning, customize clusters, install additional Hadoop projects, and administer clusters

·         Integrate, analyze, and report with Microsoft BI and Power BI

·         Automate workflows for data transformation, integration, and other tasks

·         Use Apache HBase on HDInsight

·         Use Sqoop or SSIS to move data to or from HDInsight

·         Perform R-based statistical computing on HDInsight datasets

·         Accelerate analytics with Apache Spark

·         Run real-time analytics on high-velocity data streams

·         Write MapReduce, Hive, and Pig programs

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Sample Content

Table of Contents

Introduction

Part I: Understanding Big Data, Hadoop 1.0, and 2.0

Hour 1: Introduction of Big Data, NoSQL, and Business Value Proposition

Types of Analysis

Types of Data

Big Data

Managing Big Data

NoSQL Systems

Big Data, NoSQL Systems, and the Business Value Proposition

Application of Big Data and Big Data Solutions

Summary

Q&A

Hour 2: Introduction to Hadoop, Its Architecture, Ecosystem, and Microsoft Offerings

What Is Apache Hadoop?

Architecture of Hadoop and Hadoop Ecosystems

What’s New in Hadoop 2.0

Architecture of Hadoop 2.0

Tools and Technologies Needed with Big Data Analytics

Major Players and Vendors for Hadoop

Deployment Options for Microsoft Big Data Solutions

Summary

Q&A

Hour 3: Hadoop Distributed File System Versions 1.0 and 2.0

Introduction to HDFS

HDFS Architecture

Rack Awareness

WebHDFS

Accessing and Managing HDFS Data

What’s New in HDFS 2.0

Summary

Q&A

Hour 4: The MapReduce Job Framework and Job Execution Pipeline

Introduction to MapReduce

MapReduce Architecture

MapReduce Job Execution Flow

Summary

Q&A

Hour 5: MapReduce–Advanced Concepts and YARN

DistributedCache

Hadoop Streaming

MapReduce Joins

Bloom Filter

Performance Improvement

Handling Failures

Counter

YARN

Uber-Tasking Optimization

Failures in YARN

Resource Manager High Availability and Automatic Failover in YARN

Summary

Q&A

Part II: Getting Started with HDInsight and Understanding Its Different Components

Hour 6: Getting Started with HDInsight, Provisioning Your HDInsight Service Cluster, and Automating HDInsight Cluster Provisioning

Introduction to Microsoft Azure

Understanding HDInsight Service

Provisioning HDInsight on the Azure Management Portal

Automating HDInsight Provisioning with PowerShell

Managing and Monitoring HDInsight Cluster and Job Execution

Summary

Q&A

Exercise

Hour 7: Exploring Typical Components of HDFS Cluster

HDFS Cluster Components

HDInsight Cluster Architecture

High Availability in HDInsight

Summary

Q&A

Hour 8: Storing Data in Microsoft Azure Storage Blob

Understanding Storage in Microsoft Azure

Benefits of Azure Storage Blob over HDFS

Azure Storage Explorer Tools

Summary

Q&A

Hour 9: Working with Microsoft Azure HDInsight Emulator

Getting Started with HDInsight Emulator

Setting Up Microsoft Azure Emulator for Storage

Summary

Q&A

Part III: Programming MapReduce and HDInsight Script Action

Hour 10: Programming MapReduce Jobs

MapReduce Hello World!

Analyzing Flight Delays with MapReduce

Serialization Frameworks for Hadoop

Hadoop Streaming

Summary

Q&A

Hour 11: Customizing the HDInsight Cluster with Script Action

Identifying the Need for Cluster Customization

Developing Script Action

Consuming Script Action

Running a Giraph job on a Customized HDInsight Cluster

Testing Script Action with HDInsight Emulator

Summary

Q&A

Part IV: Querying and Processing Big Data in HDInsight

Hour 12: Getting Started with Apache Hive and Apache Tez in HDInsight

Introduction to Apache Hive

Getting Started with Apache Hive in HDInsight

Azure HDInsight Tools for Visual Studio

Programmatically Using the HDInsight .NET SDK

Introduction to Apache Tez

Summary

Q&A

Exercise

Hour 13: Programming with Apache Hive, Apache Tez in HDInsight, and Apache HCatalog

Programming with Hive in HDInsight

Using Tables in Hive

Serialization and Deserialization

Data Load Processes for Hive Tables

Querying Data from Hive Tables

Indexing in Hive

Apache Tez in Action

Apache HCatalog

Summary

Q&A

Exercise

Hour 14: Consuming HDInsight Data from Microsoft BI Tools over Hive ODBC Driver: Part 1

Introduction to Hive ODBC Driver

Introduction to Microsoft Power BI

Accessing Hive Data from Microsoft Excel

Summary

Q&A

Hour 15: Consuming HDInsight Data from Microsoft BI Tools over Hive ODBC Driver: Part 2

Accessing Hive Data from PowerPivot

Accessing Hive Data from SQL Server

Accessing HDInsight Data from Power Query

Summary

Q&A

Exercise

Hour 16: Integrating HDInsight with SQL Server Integration Services

The Need for Data Movement

Introduction to SSIS

Analyzing On-time Flight Departure with SSIS

Provisioning HDInsight Cluster

Summary

Q&A

Hour 17: Using Pig for Data Processing

Introduction to Pig Latin

Using Pig to Count Cancelled Flights

Using HCatalog in a Pig Latin Script

Submitting Pig Jobs with PowerShell

Summary

Q&A

Hour 18: Using Sqoop for Data Movement Between RDBMS and HDInsight

What Is Sqoop?

Using Sqoop Import and Export Commands

Using Sqoop with PowerShell

Summary

Q&A

Part V: Managing Workflow and Performing Statistical Computing

Hour 19: Using Oozie Workflows and Job Orchestration with HDInsight

Introduction to Oozie

Determining On-time Flight Departure Percentage with Oozie

Submitting an Oozie Workflow with HDInsight .NET SDK

Coordinating Workflows with Oozie

Oozie Compared to SSIS

Summary

Q&A

Hour 20: Performing Statistical Computing with R

Introduction to R

Integrating R with Hadoop

Enabling R on HDInsight

Summary

Q&A

Part VI: Performing Interactive Analytics and Machine Learning

Hour 21: Performing Big Data Analytics with Spark

Introduction to Spark

Spark Programming Model

Blending SQL Querying with Functional Programs

Summary

Q&A

Hour 22: Microsoft Azure Machine Learning

History of Traditional Machine Learning

Introduction to Azure ML

Azure ML Workspace

Processes to Build Azure ML Solutions

Getting Started with Azure ML

Creating Predictive Models with Azure ML

Publishing Azure ML Models as Web Services

Summary

Q&A

Exercise

Part VII: Performing Real-time Analytics

Hour 23: Performing Stream Analytics with Storm

Introduction to Storm

Using SCP.NET to Develop Storm Solutions

Analyzing Speed Limit Violation Incidents with Storm

Summary

Q&A

Hour 24: Introduction to Apache HBase on HDInsight

Introduction to Apache HBase

HBase Architecture

Creating HDInsight Cluster with HBase

Summary

Q&A

9780672337277   TOC   10/26/2015

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