MapR redefines SQL-on-Hadoop with Apache Drill

MapR Technologies, Inc. has announced the addition of Apache Drill 0.5 to the MapR Distribution including Hadoop. Bringing next-generation ANSI SQL to Hadoop, Apache Drill provides instant, self-service data exploration across multiple data sources including modern applications.

“Organizations want to provide access to data stored in Hadoop and NoSQL databases to a broader set of users with existing SQL analysis skills,” said Matt Aslett, research director, data platforms and analytics, 451 Research. "Apache Drill's ability to provide access to data in Hadoop without the need for centralized schemas and also to NoSQL datasets with complex data structures including nested and repeated fields differentiates it from traditional approaches to SQL-on-Hadoop."

Apache Drill provides the flexibility to immediately query complex data in native formats, such as schema-less data, nested data, and data with rapidly-evolving schemas, with minimal IT involvement. Because SQL queries can run directly on various file formats, live data can be explored as it is coming in, versus spending weeks preparing and managing schemas and setting up ETL tasks. Additionally, Apache Drill supports ANSI SQL so users can easily leverage their SQL skills and existing investments in business intelligence (BI) tools.

“The vision and innovation that the Apache Drill community has brought to the marketplace heralds a new era of data exploration,” said John Schroeder, CEO and cofounder of MapR Technologies. “The agility to directly query self-describing data and the flexibility to process complex data types push the envelope in big data analysis and insight. We are extremely excited by the potential of Drill to transform data-driven companies.”

Organizations that use Apache Drill benefit from:
· High-performance analysis of data in its native format including self-describing data such as Parquet, JSON files and HBase tables
· Direct querying of data in HBase tables without defining and maintaining a parallel/overlay schema in the Hive metastore
· Intuitive SQL extensions to query and work with semi-structured/nested data, such as data from NoSQL stores like MongoDB and online REST APIs
· Queries that simultaneously combine different Hadoop data sources such as files, HBase tables, and Hive tables
Developers and analysts can leverage existing SQL skillsets and BI tools to:
· Minimize switching costs and the learning curve for users via the familiar ANSI SQL syntax
· Continue using familiar BI/analytics tools such as Excel, Tableau and a host of others using standard ODBC/JDBC drivers
· Enable ad-hoc/low-latency queries on existing Hive tables. Reuse Hive metadata, hundreds of file formats and user defined functions (UDFs) out of the box