February 25, 2013, 11:20 AM — One commonly cited stumbling block to the broader adoption of Apache Hadoop in the enterprise is the difficulty and expense of finding and hiring developers who understand and can think in Hadoop MapReduce. But Big Data application framework specialist Concurrent wants to change that.
Cascading gives Java developers the capability to build Big Data applications on Hadoop using their existing skillset. Now Concurrent hopes to help SQL users get into the act with Lingual, an open source ANSI-standard SQL engine that runs on top of Cascading.
Lingual Lets Analysts, Developers Tap SQL Skills for Hadoop
Lingual, which will be publicly available under the Apache 2.0 license within the next few weeks, gives analysts and developers familiar with SQL, JDBC and traditional BI tools the capability to create and run Big Data applications on Hadoop using their existing skillsets.
"Concurrent was established with the belief that there had to be a simpler path to mass Hadoop adoption," Wensel says. "And since day one, we have worked to create solutions that make it easier for developers to build powerful and robust Big Data applications quickly and easily. With the Lingual project, we are one huge step closer to realizing our mission."
To date, many Hadoop users have turned to Apache Hive (a data warehouse infrastructure built for Hadoop) and Apache Pig (a high-level platform for creating MapReduce programs) to achieve SQL-like capabilities.
"Pig and Hive have their own qualities and actually are quite good, but sometimes you just want SQL," Wensel says. "Lingual is great for people who don't know how to use Hadoop but know SQL. The best way to get value out of something in many cases is just to use SQL."
"We just want to make it easier for people to get data off Hadoop or to port their apps to Hadoop using skills they already know," he adds.
Use Cases for Open Source Lingual SQL Parser
Example use cases for Lingual include the following: