Hadoop Ecosystem: SQOOP – The Data Mover

Apache Sqoop Logo

Sqoop Logo

 SQOOP is an open source project hosted by the Apache Foundation whose objective is to provide a tool that will allow users to move large volumes of data in bulk from structured data sources into the Hadoop Distributed File System (HDFS). The project graduated from the Apache Incubator in March of 2012 and it is now a Top-Level Apache project.

The best way to look at Sqoop is as a collection of related tools where each of these sub-modules serves a specific use case such as importing into Hive or leveraging parallelism when reading from a MySQL database. You do specify the tool you are invoking when you use Sqoop. In terms of syntax, each of these tools have a specific set of arguments while supporting global arguments as well.

 

Below is a list of the most frequently used Sqoop tools as of version 1.4.5 with a brief description of their purpose:

 

  • Sqoop import: Helps users import a single table into Hadoop
  • Sqoop import-all-tables: Imports all tables in a database schema into Hadoop
  • Sqoop export: Allows users to export a set of files from HDFS back into a relational database
  • Sqoop create-hive-table: Allows users to import relational data directly into Apache Hive

Hadoop Ecosystem: Hive – the Data Warehouse and SQL interface

Apache Hive

Apache Hive

The Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.

 

Hive is both a metadata layer on top of HDFS and a SQL interpreter. This allows companies to store structured or semi-structured data as files on Hadoop without a large initial data modeling effort, once business requirements align with the need to extract new insights from the stored data a development team can leverage the “schema on read” paradigm to create metadata about these files.

 

Having a SQL interpreter allows business analysts and power users to have access to terabytes or petabytes of information through a familiar query language. This is a dramatic departure from MapReduce where a very specialized skill set would be required to write multiple Map and Reduce functions in order to achieve the same results.