On March 9, the Apache Software Foundation announced the availability of Apache Tajo version 10.0. Less well known than its counterpart Apache Hive, Apache Tajo is used for ETL on big data in addition to SQL-compliant querying functionality that delivers scalable, low latency results. Version 10.0 features enhancements to Amazon S3 and an improved JDBC driver that renders Tajo compatible with most major BI platforms. Hyunsik Choi, Vice President of Apache Tajo, remarked on Apache Tajo’s progress as follows:
Tajo has evolved over the last couple of years into a mature ‘SQL-on-Hadoop’ engine. The improved JDBC driver in this release allows users to easily access Tajo as if users use traditional RDBMSs. We have verified new JDBC driver on many commercial BI solutions and various SQL tools. It was easy and works successfully.
As Choi notes, Tajo attempts to bring the simplicity and standardization of SQL and RDBMS infrastructures to the power of Hadoop’s distributed processing and scalability. Designed with a focus on fault tolerance, scalability, high throughput and query optimization, Tajo aims to deliver low latency in conjunction with a storage agnostic platform that notably boasts Hbase storage integration that allows Tajo users to access Hbase via Tajo as of this version. Tajo plays in an increasingly crowded SQL on Hadoop-space featuring the likes of Hive, Cloudera’s Impala, Pivotal HAWQ and Stinger although it claims some early adoption in South Korea, the country of its origin, with organizations such as Korea University, Melon, NASA JPL Radio Astronomy and Airborne Snow Observatory projects, and SK Telecom. The key question for Apache Tajo now is whether its new release will usher in greater traction outside of South Korea, particularly given its enhanced integration with Amazon S3 and Amazon’s Elastic Mapreduce (EMR) platform.