A great strength of the Hadoop platform is its ability to work with data in several different forms. HDFS can reliably store logs and other data from a plethora of sources, and MapReduce programs can parse diverse ad hoc data formats, extracting relevant information and combining multiple datasets into powerful results.
But to interact with data in storage repositories outside of HDFS, MapReduce programs need to use external APIs to get to this data. Often, valuable data in an organization is stored in structured data stores such as relational database systems (RDBMS). Apache Sqoop is an open source tool that allows users to extract data from a structured data store into Hadoop for further processing. This processing can be done with MapReduce programs or other higher-level tools such as Hive. (It’s even possible to use Sqoop to move data from a database into HBase.) When the final results of an analytic pipeline are available, Sqoop can export these results back to the data store for consumption by other clients.
In this chapter, we’ll take a look at how Sqoop works and how you can use it in your data processing pipeline.
Sqoop is available in a few places. The primary home of the project is http://sqoop.apache.org/. This repository contains all the Sqoop source code and documentation. Official releases are available at this site, as well as the source code for the version currently under development. The repository itself contains instructions ...