Details

Introduction xv <p><b>Part I What Is Big Data? 1</b></p> <p><b>Chapter 1 Industry Needs and Solutions 3</b></p> <p>What’s So Big About Big Data? 4</p> <p>A Brief History of Hadoop 5</p> <p>Google 5</p> <p>Nutch 6</p> <p>What Is Hadoop? 6</p> <p>Derivative Works and Distributions 7</p> <p>Hadoop Distributions 8</p> <p>Core Hadoop Ecosystem 9</p> <p>Important Apache Projects for Hadoop 11</p> <p>The Future for Hadoop 17</p> <p>Summary 17</p> <p><b>Chapter 2 Microsoft’s Approach to Big Data 19</b></p> <p>A Story of “Better Together” 19</p> <p>Competition in the Ecosystem 20</p> <p>SQL on Hadoop Today 21</p> <p>Hortonworks and Stinger 21</p> <p>Cloudera and Impala 23</p> <p>Microsoft’s Contribution to SQL in Hadoop 25</p> <p>Deploying Hadoop 25</p> <p>Deployment Factors 26</p> <p>Deployment Topologies 29</p> <p>Deployment Scorecard 33</p> <p>Summary 36</p> <p><b>Part II Setting Up for Big Data with Microsoft 37</b></p> <p><b>Chapter 3 Configuring Your First Big Data Environment 39</b></p> <p>Getting Started 39</p> <p>Getting the Install 40</p> <p>Running the Installation 40</p> <p>On-Premise Installation: Single-Node Installation 41</p> <p>HDInsight Service: Installing in the Cloud 51</p> <p>Windows Azure Storage Explorer Options 52</p> <p>Validating Your New Cluster 55</p> <p>Logging into HDInsight Service 55</p> <p>Verify HDP Functionality in the Logs 57</p> <p>Common Post-Setup Tasks 58</p> <p>Loading Your First Files 58</p> <p>Verifying Hive and Pig 60</p> <p>Summary 63</p> <p><b>Part III Storing and Managing Big Data 65</b></p> <p><b>Chapter 4 HDFS, Hive, HBase, and HCatalog 67</b></p> <p>Exploring the Hadoop Distributed File System 68</p> <p>Explaining the HDFS Architecture 69</p> <p>Interacting with HDFS 72</p> <p>Exploring Hive: The Hadoop Data Warehouse Platform 75</p> <p>Designing, Building, and Loading Tables 76</p> <p>Querying Data 77</p> <p>Configuring the Hive ODBC Driver 77</p> <p>Exploring HCatalog: HDFS Table and Metadata Management 78</p> <p>Exploring HBase: An HDFS Column-Oriented Database 80</p> <p>Columnar Databases 81</p> <p>Defining and Populating an HBase Table 82</p> <p>Using Query Operations 83</p> <p>Summary 84</p> <p><b>Chapter 5 Storing and Managing Data in HDFS 85</b></p> <p>Understanding the Fundamentals of HDFS 86</p> <p>HDFS Architecture 87</p> <p>NameNodes and DataNodes 89</p> <p>Data Replication 90</p> <p>Using Common Commands to Interact with HDFS 92</p> <p>Interfaces for Working with HDFS 92</p> <p>File Manipulation Commands 94</p> <p>Administrative Functions in HDFS 97</p> <p>Moving and Organizing Data in HDFS 100</p> <p>Moving Data in HDFS 100</p> <p>Implementing Data Structures for Easier Management 101</p> <p>Rebalancing Data 102</p> <p>Summary 103</p> <p><b>Chapter 6 Adding Structure with Hive 105</b></p> <p>Understanding Hive’s Purpose and Role 106</p> <p>Providing Structure for Unstructured Data 107</p> <p>Enabling Data Access and Transformation 114</p> <p>Differentiating Hive from Traditional RDBMS Systems 115</p> <p>Working with Hive 116</p> <p>Creating and Querying Basic Tables 117</p> <p>Creating Databases 117</p> <p>Creating Tables 118</p> <p>Adding and Deleting Data 121</p> <p>Querying a Table 123</p> <p>Using Advanced Data Structures with Hive 126</p> <p>Setting Up Partitioned Tables 126</p> <p>Loading Partitioned Tables 128</p> <p>Using Views 129</p> <p>Creating Indexes for Tables 130</p> <p>Summary 131</p> <p><b>Chapter 7 Expanding Your Capability with HBase and HCatalog 133</b></p> <p>Using HBase 134</p> <p>Creating HBase Tables 134</p> <p>Loading Data into an HBase Table 136</p> <p>Performing a Fast Lookup 138</p> <p>Loading and Querying HBase 139</p> <p>Managing Data with HCatalog 140</p> <p>Working with HCatalog and Hive 140</p> <p>Defining Data Structures 141</p> <p>Creating Indexes 143</p> <p>Creating Partitions 143</p> <p>Integrating HCatalog with Pig and Hive 145</p> <p>Using HBase or Hive as a Data Warehouse 149</p> <p>Summary 150</p> <p><b>Part IV Working with Your Big Data 151</b></p> <p><b>Chapter 8 Effective Big Data ETL with SSIS, Pig, and Sqoop 153</b></p> <p>Combining Big Data and SQL Server Tools for Better Solutions 154</p> <p>Why Move the Data? 154</p> <p>Transferring Data Between Hadoop and SQL Server 155</p> <p>Working with SSIS and Hive 156</p> <p>Connecting to Hive 157</p> <p>Configuring Your Packages 161</p> <p>Loading Data into Hadoop 165</p> <p>Getting the Best Performance from SSIS 167</p> <p>Transferring Data with Sqoop 167</p> <p>Copying Data from SQL Server 168</p> <p>Copying Data to SQL Server 170</p> <p>Using Pig for Data Movement 171</p> <p>Transforming Data with Pig 171</p> <p>Using Pig and SSIS Together 174</p> <p>Choosing the Right Tool 175</p> <p>Use Cases for SSIS 175</p> <p>Use Cases for Pig 175</p> <p>Use Cases for Sqoop 176</p> <p>Summary 176</p> <p><b>Chapter 9 Data Research and Advanced Data Cleansing with Pig and Hive 177</b></p> <p>Getting to Know Pig 178</p> <p>When to Use Pig 178</p> <p>Taking Advantage of Built-in Functions 179</p> <p>Executing User-defi ned Functions 180</p> <p>Using UDFs 182</p> <p>Building Your Own UDFs for Pig 189</p> <p>Using Hive 192</p> <p>Data Analysis with Hive 192</p> <p>Types of Hive Functions 192</p> <p>Extending Hive with Map-reduce Scripts 195</p> <p>Creating a Custom Map-reduce Script 198</p> <p>Creating Your Own UDFs for Hive 199</p> <p>Summary 201</p> <p><b>Part V Big Data and SQL Server Together 203</b></p> <p><b>Chapter 10 Data Warehouses and Hadoop Integration 205</b></p> <p>State of the Union 206</p> <p>Challenges Faced by Traditional Data Warehouse Architectures 207</p> <p>Technical Constraints 207</p> <p>Business Challenges 213</p> <p>Hadoop’s Impact on the Data Warehouse Market 216</p> <p>Keep Everything 216</p> <p>Code First (Schema Later) 217</p> <p>Model the Value 218</p> <p>Throw Compute at the Problem 218</p> <p>Introducing Parallel Data Warehouse (PDW) 220</p> <p>What Is PDW? 221</p> <p>Why Is PDW Important? 222</p> <p>How PDW Works 224</p> <p>Project Polybase 235</p> <p>Polybase Architecture 235</p> <p>Business Use Cases for Polybase Today 249</p> <p>Speculating on the Future for Polybase 251</p> <p>Summary 255</p> <p><b>Chapter 11 Visualizing Big Data with Microsoft BI 257</b></p> <p>An Ecosystem of Tools 258</p> <p>Excel 258</p> <p>PowerPivot 258</p> <p>Power View 259</p> <p>Power Map 261</p> <p>Reporting Services 261</p> <p>Self-service Big Data with PowerPivot 263</p> <p>Setting Up the ODBC Driver 263</p> <p>Loading Data 265</p> <p>Updating the Model 272</p> <p>Adding Measures 273</p> <p>Creating Pivot Tables 274</p> <p>Rapid Big Data Exploration with Power View 277</p> <p>Spatial Exploration with Power Map 281</p> <p>Summary 283</p> <p><b>Chapter 12 Big Data Analytics 285</b></p> <p>Data Science, Data Mining, and Predictive Analytics 286</p> <p>Data Mining 286</p> <p>Predictive Analytics 287</p> <p>Introduction to Mahout 288</p> <p>Building a Recommendation Engine 289</p> <p>Getting Started 291</p> <p>Running a User-to-user Recommendation Job 292</p> <p>Running an Item-to-item Recommendation Job 295</p> <p>Summary 296</p> <p><b>Chapter 13 Big Data and the Cloud 297</b></p> <p>Defi ning the Cloud 298</p> <p>Exploring Big Data Cloud Providers 299</p> <p>Amazon 299</p> <p>Microsoft 300</p> <p>Setting Up a Big Data Sandbox in the Cloud 300</p> <p>Getting Started with Amazon EMR 301</p> <p>Getting Started with HDInsight 307</p> <p>Storing Your Data in the Cloud 315</p> <p>Storing Data 316</p> <p>Uploading Your Data 317</p> <p>Exploring Big Data Storage Tools 318</p> <p>Integrating Cloud Data 319</p> <p>Other Cloud Data Sources 321</p> <p>Summary 321</p> <p><b>Chapter 14 Big Data in the Real World 323</b></p> <p>Common Industry Analytics 324</p> <p>Telco 324</p> <p>Energy 325</p> <p>Retail 325</p> <p>Data Services 326</p> <p>IT/Hosting Optimization 326</p> <p>Marketing Social Sentiment 327</p> <p>Operational Analytics 327</p> <p>Failing Fast 328</p> <p>A New Ecosystem of Technologies 328</p> <p>User Audiences 330</p> <p>Summary 333</p> <p><b>Part VI Moving Your Big Data Forward 335</b></p> <p><b>Chapter 15 Building and Executing Your Big Data Plan 337</b></p> <p>Gaining Sponsor and Stakeholder Buy-In 338</p> <p>Problem Definition 338</p> <p>Scope Management 339</p> <p>Stakeholder Expectations 341</p> <p>Defining the Criteria for Success 342</p> <p>Identifying Technical Challenges 342</p> <p>Environmental Challenges 342</p> <p>Challenges in Skillset 344</p> <p>Identifying Operational Challenges 345</p> <p>Planning for Setup/Configuration 345</p> <p>Planning for Ongoing Maintenance 347</p> <p>Going Forward 348</p> <p>The HandOff to Operations 348</p> <p>After Deployment 349</p> <p>Summary 350</p> <p><b>Chapter 16 Operational Big Data Management 351</b></p> <p>Hybrid Big Data Environments: Cloud and On-Premise Solutions Working Together 352</p> <p>Ongoing Data Integration with Cloud and On-Premise Solutions 353</p> <p>Integration Thoughts for Big Data 354</p> <p>Backups and High Availability in</p> <p>Your Big Data Environment 356</p> <p>High Availability 356</p> <p>Disaster Recovery 358</p> <p>Big Data Solution Governance 359</p> <p>Creating Operational Analytics 360</p> <p>System Center Operations Manager for HDP 361</p> <p>Installing the Ambari SCOM Management Pack 362</p> <p>Monitoring with the Ambari SCOM Management Pack 371</p> <p>Summary 377</p> <p>Index 379</p>

<p><b>Adam Jorgensen</b> is the President of Pragmatic Works and the Executive Vice President of PASS. He has extensive experience with data warehousing, analytics, and NoSQL architectures.</p> <p><b>James Rowland-Jones</b> 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.</p> <p><b>John Welch</b> 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.</p> <p><b>Dan Clark</b> is a senior BI consultant for Pragmatic Works. Dan has published several books and numerous articles on .NET programming and BI development.</p> <p><b>Christopher Price</b> is a senior consultant with Microsoft. His focus is on ETL, data integration, data quality, MDM, SSAS, SharePoint, and all things big data.</p> <p><b>Brian Mitchell</b> is the lead architect of the Microsoft Big Data Center of Expertise. He focuses exclusively on DW/BI solutions.</p>

<p><b>Implement Big Data solutions with powerful Microsoft tools</b></p> <p>Microsoft’s powerful big data platform—Windows Azure HDInsight and Hortonworks Data Platform for Windows—can transform the way your organization processes, stores, and manages enterprise data. Designed to work seamlessly with your company’s existing data infrastructure and with products like SQL Server and Hadoop, Microsoft’s suite of big data solutions can be implemented without disrupting workflow or critical processes. If you need a detailed roadmap for designing and implementing a fully deployed big data solution, look no further than <i>Microsoft Big Data Solutions</i>.</p> <ul> <li>Integrate big data solutions in your company using Windows Azure® HDInsight®, Hortonworks® Data Platform for Windows, and open source tools</li> <li>Store, manage, analyze, and share big data throughout your organization</li> <li>Install and configure Hortonworks Data Platform for Windows</li> <li>Learn to integrate big data with SQL Server® and Hadoop</li> <li>Visualize data with Microsoft and Microsoft and Hadoop BI tools</li> <li>Create and execute a comprehensive big data strategy for your enterprise</li> <li>Get leading-edge insights directly from the Microsoft big data product team</li> </ul>

US