Apache Impala Guide

Introducing Apache Impala

Impala provides fast, interactive SQL queries directly on your Apache Hadoop data stored in HDFS, HBase, or the Amazon Simple Storage Service (S3). In addition to using the same unified storage platform, Impala also uses the same metadata, SQL syntax (Hive SQL),ODBC driver, and userinterface (Impala query UI in Hue) as Apache Hive. This provides a familiar and unified platform for real-time or batch-oriented queries.

Impala is an addition to tools available for querying big data. Impala does not replace the batch processing frameworks built on MapReduce such as Hive. Hive and other frameworks built on MapReduce are best suited for long running batch jobs, such as those involving batch processing of Extract, Transform, and Load (ETL) type jobs.

Impala Benefits

Impala provides:

• Familiar SQL interface that data scientists and analysts already know.
• Ability to query high volumes of data (“big data”) in Apache Hadoop.
• Distributed queries in a cluster environment, for convenient scaling and to make use of cost-effective commodity hardware.
• Ability to share data files between different components with no copy or export/import step; for example, to write with Pig, transform with Hive and query with Impala. Impala can read from and write to Hive tables, enabling simple data interchange using Impala for analytics on Hive-produced data.
• Single system for big data processing and analytics, so customers can avoid costly modeling and ETL just for analytics.