Book Image

Microsoft SQL Server 2012 with Hadoop

By : Debarchan Sarkar
Book Image

Microsoft SQL Server 2012 with Hadoop

By: Debarchan Sarkar

Overview of this book

With the explosion of data, the open source Apache Hadoop ecosystem is gaining traction, thanks to its huge ecosystem that has arisen around the core functionalities of its distributed file system (HDFS) and Map Reduce. As of today, being able to have SQL Server talking to Hadoop has become increasingly important because the two are indeed complementary. While petabytes of unstructured data can be stored in Hadoop taking hours to be queried, terabytes of structured data can be stored in SQL Server 2012 and queried in seconds. This leads to the need to transfer and integrate data between Hadoop and SQL Server. Microsoft SQL Server 2012 with Hadoop is aimed at SQL Server developers. It will quickly show you how to get Hadoop activated on SQL Server 2012 (it ships with this version). Once this is done, the book will focus on how to manage big data with Hadoop and use Hadoop Hive to query the data. It will also cover topics such as using in-memory functions by SQL Server and using tools for BI with big data. Microsoft SQL Server 2012 with Hadoop focuses on data integration techniques between relational (SQL Server 2012) and non-relational (Hadoop) worlds. It will walk you through different tools for the bi-directional movement of data with practical examples. You will learn to use open source connectors like SQOOP to import and export data between SQL Server 2012 and Hadoop, and to work with leading in-memory BI tools to create ETL solutions using the Hive ODBC driver for developing your data movement projects. Finally, this book will give you a glimpse of the present day self-service BI tools such as Excel and PowerView to consume Hadoop data and provide powerful insights on the data.
Table of Contents (12 chapters)

Summary


In this chapter, we had a dive into the Hadoop supporting project Hive. Hive acts as a data warehouse on top of HDFS providing easy, familiar SQL-like query structures called HQL to fetch the underlying data. HQLs are broken down into MapReduce code internally, thus relieving the end user from writing complex MapReduce code. We also learned about the Hive ODBC driver that acts as an interface between the client consumers and Hadoop; how to install the driver and how to test that the driver is successfully able to connect to Hive. We had a brief look into SQL Server and its business intelligence components as well in this chapter. We developed a sample package, which connects to Hive using the Hive ODBC driver and imports data from the Hive table facebookinsights to SQL Server. Once the data is in SQL Server, we can leverage warehousing solutions such as SQL Server Analysis Services (SSAS) to slice and dice the data as well as SQL Server Reporting Services (SSRS) for powerful reporting...