UPDATED 07:35 EDT / MARCH 11 2015

Apache Tajo’s Big Data warehouse comes to Hadoop

sports-hall-95272_640The Apache Software Foundation (ASF) has just announced the relatively unknown Apache Tajo open source data warehouse software is ready for commercial use, almost two years after it first became a top-level project, and five years after its development began.

Tajo is an SQL-on-Hadoop platform that’s designed to help organizations extract more intelligence from their Hadoop deployments, and its first official release comes with updates that provide more connectivity to third-party databases like Oracle and PostGreSQL, plus Java programs. The software helps analyze data stored on the Hadoop Distributed File System (HDFS), as well as other data sources like Amazon S3, Openstack Swift and local file systems. Although less well-known, it’s somewhat similar to solutions like Apache Hive and Cloudera’s Impala, providing an extract, transform and load (ETL) feature set and an extensible query re-write system that lets users and external programs query data through SQL.

The software could be a good fit for organizations that have outgrown their commercial data warehouses, writes Joab Jackson in Computerworld. On top of that, it could also be a useful solution for companies looking to analyze Hadoop data using more familiar commercial businesses intelligence tools, rather than the MapReduce framework.

Unusually for an Apache project, Tajo traces its roots back to South Korea, where a Big Data infrastructure startup called Gruter is leading its development. That probably explains why Tajo is less well known than Hive or Impala, but it hasn’t escaped the attention of Cloudera Inc., Hortonworks Inc., Intel Corp., and NASA, all of whom have engineers contributing to the project.

Tajo’s most famous user is the Korean telecom firm SK Telcom Co., Ltd., which first deployed the software for Big Data analytics in 2013. The company claims Tajo offers a distinct speed advantage over Hive, boosting data processing times by a factor of 3.7 while the overall workload decreased by 70 percent, according to ZDNet. Another user is the Korean music streaming service Melon, which uses Tajo for analytical processing and claims it can execute ETL jobs up to 10 times faster than Hive.

Of course, such benchmarks must be taken with a pinch of salt as the results can often vary depending on the type of workload performed. It’s also worth noting that the latest versions of Hive and Impala may well have closed the speed gap substantially since the test was performed.

Image credit: Hans via Pixabay.com


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