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...