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

With Microsoft HDInsight, business professionals and data analysts can rapidly leverage the power of Hadoop on a flexible, scalable cloud-based platform, using Microsoft’s accessible business intelligence, visualization, and productivity tools. Now, in just 24 lessons of one hour or less, you can learn all the skills and techniques you’ll need to provision, configure, monitor, troubleshoot, and use HDInsight, even if you’re new to big data analytics. Each short, easy lesson builds on all that’s come before: you’ll learn all of HDInsight’s essentials as you solve real data analytics problems. Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours covers all this, and much more:

• Introduction of Big Data, NoSQL systems, its Business Value Proposition and use cases examples
• Introduction to Hadoop, Architecture, Ecosystem and Microsoft HDInsight
• Getting to know Hadoop 2.0 and the innovations it provides like HDFS2 and YARN
• Quickly installing, configuring, and monitoring Hadoop (HDInsight) clusters in the cloud and automating cluster provisioning
• Customize the HDInsight cluster and install additional Hadoop ecosystem projects using Script Actions
• Administering HDInsight from the Hadoop command prompt or Microsoft PowerShell
• Using the Microsoft Azure HDInsight Emulator for learning or development
• Understanding HDFS, HDFS vs. Azure Blob Storage, MapReduce Job Framework and Job Execution Pipeline
• Doing big data analytics with MapReduce, writing your MapReduce programs in your choice of .NET programming language such as C#
• Using Hive for big data analytics, demonstrate end to end scenario and how Apache Tez improves the performance several folds
• Consuming HDInsight data from Microsoft BI Tools over Hive ODBC Driver – Using HDInsight with Microsoft BI and Power BI to simplify data integration, analysis, and reporting
• Using PIG for big data transformation workflows step by step
• Apache HBase on HDInsight, its architecture, data model, HBase vs. Hive, programmatically managing HBase data with C# and Apache Phoenix
• Using Sqoop or SSIS (SQL Server Integration Services) to move data to/from HDInsight and build data integration workflows for transferring data
• Using Oozie for scheduling, co-ordination and managing data processing workflows in HDInsight cluster
• Using R programming language with HDInsight for performing statistical computing on Big Data sets
• Using Apache Spark’s in-memory computation model to run big data analytics up to 100 times faster than Hadoop MapReduce
• Perform real-time Stream Analytics on high-velocity big data streams with Storm
• Integration of Enterprise Data Warehouse with Hadoop and Microsoft Analytics Platform System (APS), formally known as SQL Server Parallel Data Warehouse (PDW)

Step-by-step instructions walk you through common questions, issues, and tasks; Q-and-As, Quizzes, and Exercises build and test your knowledge; “Did You Know?” tips offer insider advice and shortcuts; and “Watch Out!” alerts help you avoid problems. By the time you’re finished, you’ll be comfortable going beyond the book to create any HDInsight app you can imagine!

Table of Contents

Part I: Understanding Big Data, Hadoop 1.0, and 2.0
Hour 1. Introduction of Big Data, NoSQL, and Business Value Proposition
Hour 2. Introduction to Hadoop, Its Architecture, Ecosystem, and Microsoft Offerings
Hour 3. Hadoop Distributed File System Versions 1.0 and 2.0
Hour 4. The MapReduce Job Framework and Job Execution Pipeline
Hour 5. MapReduce—Advanced Concepts and YARN

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
Hour 7. Exploring Typical Components of HDFS Cluster
Hour 8. Storing Data in Microsoft Azure Storage Blob
Hour 9. Working with Microsoft Azure HDInsight Emulator

Part III: Programming MapReduce and HDInsight Script Action
Hour 10. Programming MapReduce Jobs
Hour 11. Customizing the HDInsight Cluster with Script Action

Part IV: Querying and Processing Big Data in HDInsight
Hour 12. Getting Started with Apache Hive and Apache Tez in HDInsight
Hour 13. Programming with Apache Hive, Apache Tez in HDInsight, and Apache HCatalog
Hour 14. Consuming HDInsight Data from Microsoft BI Tools over Hive ODBC Driver: Part 1
Hour 15. Consuming HDInsight Data from Microsoft BI Tools over Hive ODBC Driver: Part 2
Hour 16. Integrating HDInsight with SQL Server Integration Services
Hour 17. Using Pig for Data Processing
Hour 18. Using Sqoop for Data Movement Between RDBMS and HDInsight

Part V: Managing Workflow and Performing Statistical Computing
Hour 19. Using Oozie Workflows and Job Orchestration with HDInsight
Hour 20. Performing Statistical Computing with R

Part VI: Performing Interactive Analytics and Machine Learning
Hour 21. Performing Big Data Analytics with Spark
Hour 22. Microsoft Azure Machine Learning

Part VII: Performing Real-time Analytics
Hour 23. Performing Stream Analytics with Storm
Hour 24. Introduction to Apache HBase on HDInsight