Microsoft Big Data Solutions

Overview

Tap the power of Big Data with Microsoft technologies

Big Data is here, and Microsoft's new Big Data platform is a valuable tool to help your company get the very most out of it. This timely book shows you how to use HDInsight along with HortonWorks Data Platform for Windows to store, manage, analyze, and share Big Data throughout the enterprise. Focusing primarily on Microsoft and HortonWorks technologies but also covering open source tools, Microsoft Big Data Solutions ...

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Overview

Tap the power of Big Data with Microsoft technologies

Big Data is here, and Microsoft's new Big Data platform is a valuable tool to help your company get the very most out of it. This timely book shows you how to use HDInsight along with HortonWorks Data Platform for Windows to store, manage, analyze, and share Big Data throughout the enterprise. Focusing primarily on Microsoft and HortonWorks technologies but also covering open source tools, Microsoft Big Data Solutions explains best practices, covers on-premises and cloud-based solutions, and features valuable case studies.

Best of all, it helps you integrate these new solutions with technologies you already know, such as SQL Server and Hadoop.

  • Walks you through how to integrate Big Data solutions in your company using Microsoft's HDInsight Server, HortonWorks Data Platform for Windows, and open source tools
  • Explores both on-premises and cloud-based solutions
  • Shows how to store, manage, analyze, and share Big Data through the enterprise
  • Covers topics such as Microsoft's approach to Big Data, installing and configuring HortonWorks Data Platform for Windows, integrating Big Data with SQL Server, visualizing data with Microsoft and HortonWorks BI tools, and more
  • Helps you build and execute a Big Data plan
  • Includes contributions from the Microsoft and HortonWorks Big Data product teams

If you need a detailed roadmap for designing and implementing a fully deployed Big Data solution, you'll want Microsoft Big Data Solutions.

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Product Details

  • ISBN-13: 9781118729083
  • Publisher: Wiley
  • Publication date: 3/3/2014
  • Edition number: 1
  • Pages: 408
  • Sales rank: 771,043
  • Product dimensions: 7.40 (w) x 9.20 (h) x 1.00 (d)

Meet the Author

Adam Jorgensen is the President of Pragmatic Works and the Executive Vice President of PASS. He has extensive experience with data warehousing, analytics, and NoSQL architectures.

James Rowland-Jones is a principal consultant for The Big Bang Data Company. He specializes in big data warehouse solutions that leverage SQL Server Parallel Data Warehouse and Hadoop ecosystems.

John Welch is Vice President of Software Development at Pragmatic Works, where he leads the development of a suite of BI and data products for SQL Server and related technologies.

Dan Clark is a senior BI consultant for Pragmatic Works. Dan has published several books and numerous articles on .NET programming and BI development.

Christopher Price is a senior consultant with Microsoft. His focus is on ETL, data integration, data quality, MDM, SSAS, SharePoint, and all things big data.

Brian Mitchell is the lead architect of the Microsoft Big Data Center of Expertise. He focuses exclusively on DW/BI solutions.

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Table of Contents

Introduction xv

Part I What Is Big Data? 1

Chapter 1 Industry Needs and Solutions 3

What’s So Big About Big Data? 4

A Brief History of Hadoop 5

Google 5

Nutch 6

What Is Hadoop? 6

Derivative Works and Distributions 7

Hadoop Distributions 8

Core Hadoop Ecosystem 9

Important Apache Projects for Hadoop 11

The Future for Hadoop 17

Summary 17

Chapter 2 Microsoft’s Approach to Big Data 19

A Story of “Better Together” 19

Competition in the Ecosystem 20

SQL on Hadoop Today 21

Hortonworks and Stinger 21

Cloudera and Impala 23

Microsoft’s Contribution to SQL in Hadoop 25

Deploying Hadoop 25

Deployment Factors 26

Deployment Topologies 29

Deployment Scorecard 33

Summary 36

Part II Setting Up for Big Data with Microsoft 37

Chapter 3 Configuring Your First Big Data Environment 39

Getting Started 39

Getting the Install 40

Running the Installation 40

On-Premise Installation: Single-Node Installation 41

HDInsight Service: Installing in the Cloud 51

Windows Azure Storage Explorer Options 52

Validating Your New Cluster 55

Logging into HDInsight Service 55

Verify HDP Functionality in the Logs 57

Common Post-Setup Tasks 58

Loading Your First Files 58

Verifying Hive and Pig 60

Summary 63

Part III Storing and Managing Big Data 65

Chapter 4 HDFS, Hive, HBase, and HCatalog 67

Exploring the Hadoop Distributed File System 68

Explaining the HDFS Architecture 69

Interacting with HDFS 72

Exploring Hive: The Hadoop Data Warehouse Platform 75

Designing, Building, and Loading Tables 76

Querying Data 77

Configuring the Hive ODBC Driver 77

Exploring HCatalog: HDFS Table and Metadata Management 78

Exploring HBase: An HDFS Column-Oriented Database 80

Columnar Databases 81

Defining and Populating an HBase Table 82

Using Query Operations 83

Summary 84

Chapter 5 Storing and Managing Data in HDFS 85

Understanding the Fundamentals of HDFS 86

HDFS Architecture 87

NameNodes and DataNodes 89

Data Replication 90

Using Common Commands to Interact with HDFS 92

Interfaces for Working with HDFS 92

File Manipulation Commands 94

Administrative Functions in HDFS 97

Moving and Organizing Data in HDFS 100

Moving Data in HDFS 100

Implementing Data Structures for Easier Management 101

Rebalancing Data 102

Summary 103

Chapter 6 Adding Structure with Hive 105

Understanding Hive’s Purpose and Role 106

Providing Structure for Unstructured Data 107

Enabling Data Access and Transformation 114

Differentiating Hive from Traditional RDBMS Systems 115

Working with Hive 116

Creating and Querying Basic Tables 117

Creating Databases 117

Creating Tables 118

Adding and Deleting Data 121

Querying a Table 123

Using Advanced Data Structures with Hive 126

Setting Up Partitioned Tables 126

Loading Partitioned Tables 128

Using Views 129

Creating Indexes for Tables 130

Summary 131

Chapter 7 Expanding Your Capability with HBase and HCatalog 133

Using HBase 134

Creating HBase Tables 134

Loading Data into an HBase Table 136

Performing a Fast Lookup 138

Loading and Querying HBase 139

Managing Data with HCatalog 140

Working with HCatalog and Hive 140

Defining Data Structures 141

Creating Indexes 143

Creating Partitions 143

Integrating HCatalog with Pig and Hive 145

Using HBase or Hive as a Data Warehouse 149

Summary 150

Part IV Working with Your Big Data 151

Chapter 8 Effective Big Data ETL with SSIS, Pig, and Sqoop 153

Combining Big Data and SQL Server Tools for Better Solutions 154

Why Move the Data? 154

Transferring Data Between Hadoop and SQL Server 155

Working with SSIS and Hive 156

Connecting to Hive 157

Configuring Your Packages 161

Loading Data into Hadoop 165

Getting the Best Performance from SSIS 167

Transferring Data with Sqoop 167

Copying Data from SQL Server 168

Copying Data to SQL Server 170

Using Pig for Data Movement 171

Transforming Data with Pig 171

Using Pig and SSIS Together 174

Choosing the Right Tool 175

Use Cases for SSIS 175

Use Cases for Pig 175

Use Cases for Sqoop 176

Summary 176

Chapter 9 Data Research and Advanced Data Cleansing with Pig and Hive 177

Getting to Know Pig 178

When to Use Pig 178

Taking Advantage of Built-in Functions 179

Executing User-defi ned Functions 180

Using UDFs 182

Building Your Own UDFs for Pig 189

Using Hive 192

Data Analysis with Hive 192

Types of Hive Functions 192

Extending Hive with Map-reduce Scripts 195

Creating a Custom Map-reduce Script 198

Creating Your Own UDFs for Hive 199

Summary 201

Part V Big Data and SQL Server Together 203

Chapter 10 Data Warehouses and Hadoop Integration 205

State of the Union 206

Challenges Faced by Traditional Data Warehouse Architectures 207

Technical Constraints 207

Business Challenges 213

Hadoop’s Impact on the Data Warehouse Market 216

Keep Everything 216

Code First (Schema Later) 217

Model the Value 218

Throw Compute at the Problem 218

Introducing Parallel Data Warehouse (PDW) 220

What Is PDW? 221

Why Is PDW Important? 222

How PDW Works 224

Project Polybase 235

Polybase Architecture 235

Business Use Cases for Polybase Today 249

Speculating on the Future for Polybase 251

Summary 255

Chapter 11 Visualizing Big Data with Microsoft BI 257

An Ecosystem of Tools 258

Excel 258

PowerPivot 258

Power View 259

Power Map 261

Reporting Services 261

Self-service Big Data with PowerPivot 263

Setting Up the ODBC Driver 263

Loading Data 265

Updating the Model 272

Adding Measures 273

Creating Pivot Tables 274

Rapid Big Data Exploration with Power View 277

Spatial Exploration with Power Map 281

Summary 283

Chapter 12 Big Data Analytics 285

Data Science, Data Mining, and Predictive Analytics 286

Data Mining 286

Predictive Analytics 287

Introduction to Mahout 288

Building a Recommendation Engine 289

Getting Started 291

Running a User-to-user Recommendation Job 292

Running an Item-to-item Recommendation Job 295

Summary 296

Chapter 13 Big Data and the Cloud 297

Defi ning the Cloud 298

Exploring Big Data Cloud Providers 299

Amazon 299

Microsoft 300

Setting Up a Big Data Sandbox in the Cloud 300

Getting Started with Amazon EMR 301

Getting Started with HDInsight 307

Storing Your Data in the Cloud 315

Storing Data 316

Uploading Your Data 317

Exploring Big Data Storage Tools 318

Integrating Cloud Data 319

Other Cloud Data Sources 321

Summary 321

Chapter 14 Big Data in the Real World 323

Common Industry Analytics 324

Telco 324

Energy 325

Retail 325

Data Services 326

IT/Hosting Optimization 326

Marketing Social Sentiment 327

Operational Analytics 327

Failing Fast 328

A New Ecosystem of Technologies 328

User Audiences 330

Summary 333

Part VI Moving Your Big Data Forward 335

Chapter 15 Building and Executing Your Big Data Plan 337

Gaining Sponsor and Stakeholder Buy-In 338

Problem Definition 338

Scope Management 339

Stakeholder Expectations 341

Defining the Criteria for Success 342

Identifying Technical Challenges 342

Environmental Challenges 342

Challenges in Skillset 344

Identifying Operational Challenges 345

Planning for Setup/Configuration 345

Planning for Ongoing Maintenance 347

Going Forward 348

The HandOff to Operations 348

After Deployment 349

Summary 350

Chapter 16 Operational Big Data Management 351

Hybrid Big Data Environments: Cloud and On-Premise Solutions Working Together 352

Ongoing Data Integration with Cloud and On-Premise Solutions 353

Integration Thoughts for Big Data 354

Backups and High Availability in

Your Big Data Environment 356

High Availability 356

Disaster Recovery 358

Big Data Solution Governance 359

Creating Operational Analytics 360

System Center Operations Manager for HDP 361

Installing the Ambari SCOM Management Pack 362

Monitoring with the Ambari SCOM Management Pack 371

Summary 377

Index 379

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