[MapR Talk] Self-Service Data Exploration and Nested Data Analytics on Hadoop - Introduction to Apache Drill

Document created by aalvarez Employee on Nov 25, 2015Last modified by aalvarez Employee on Dec 7, 2015
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Overview

In an effort to help grow organic communities interested in new technologies, MapR speakers around the world provide technical talks on numerous topics. Please browse our MapR Talks Directory  to learn how to search and request a talk in seconds.

 

Abstract

SQL is one of the most widely used languages to access, analyze, and manipulate structured data. As Hadoop gains traction within enterprise data architectures across industries, the need for SQL for both structured and loosely-structured data on Hadoop is growing rapidly. Apache Drill started off with the audacious goal of delivering consistent, millisecond ANSI SQL query capability across wide range of data formats. At a high level, this translates to two key requirements – Schema Flexibility and Performance. Apache Drill provides the users the ability to interact with big data on Hadoop much faster and far more easily using the familiar SQL language. Users are no longer dependent on central IT teams and DBAs to produce schemas and then maintain them when the structure changes for a few records. Drill alleviates the pain associated with structuring unstructured data before one gains any insights by providing a simple mechanism to query any dataset on Hadoop - be it flat files, parquet or JSON files or tables within an HBase table. This session will give you an overview of several different use cases that enterprises are testing Drill for as well as the latest news on the project.

 

KEYWORDS: SQL, Analytics, Parquet, Interactive Request This Talk

 

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