It’s been four years since, via a post to the Apache JIRA, the first version of Sqoop was released to the world as an addition to Hadoop. Since then, the project has taken several turns, most recently landing as a top-level Apache project. I’ve been amazed at how many people use this small tool for a variety of large tasks. Sqoop users have imported everything from humble test data sets to mammoth enterprise data warehouses into the Hadoop Distributed Filesystem, HDFS. Sqoop is a core member of the Hadoop ecosystem, and plug-ins are provided and supported by several major SQL and ETL vendors. And Sqoop is now part of integral ETL and processing pipelines run by some of the largest users of Hadoop.

The software industry moves in cycles. At the time of Sqoop’s origin, a major concern was in “unlocking” data stored in an organization’s RDBMS and transferring it to Hadoop. Sqoop enabled users with vast troves of information stored in existing SQL tables to use new analytic tools like MapReduce and Apache Pig. As Sqoop matures, a renewed focus on SQL-oriented analytics continues to make it relevant: systems like Cloudera Impala and Dremel-style analytic engines offer powerful distributed analytics with SQLbased languages, using the common data substrate offered by HDFS.

The variety of data sources and analytic targets presents a challenge in setting up effective data transfer pipelines. Data sources can have a variety of subtle inconsistencies: different DBMS providers may use different dialects of SQL, treat data types differently, or use distinct techniques to offer optimal transfer speeds. Depending on whether you’re importing to Hive, Pig, Impala, or your own MapReduce pipeline, you may want to use a different file format or compression algorithm when writing data to HDFS. Sqoop helps the data engineer tasked with scripting such transfers by providing a compact but powerful tool that flexibly negotiates the boundaries between these systems and their data layouts.