Apache Impala is a query engine that runs on Apache Hadoop. The project was announced in October 2012 with a public
beta test distribution and became generally available in May 2013. Impala brings scalable
parallel database technology to Hadoop, enabling users to issue low-latency
SQL queries to data stored in
HDFS and
Apache HBase without requiring data movement or transformation. Impala is integrated with Hadoop to use the same file and data formats, metadata, security and resource management frameworks used by
MapReduce,
Apache Hive,
Apache Pig and other Hadoop software. Impala is promoted for analysts and data scientists to perform analytics on data stored in Hadoop via SQL or
business intelligence tools. The result is that large-scale data processing (via MapReduce) and interactive queries can be done on the same system using the same data and metadata – removing the need to migrate data sets into specialized systems and/or proprietary formats simply to perform analysis. Features include: • Supports
HDFS,
S3,
Microsoft Azure Blob Storage,
Apache HBase and
Apache Kudu storage, • Reads Hadoop file formats, including text,
LZO,
SequenceFile,
Avro,
RCFile,
Parquet and
ORC • Supports Hadoop security (
Kerberos authentication,
Ldap), • Fine-grained, role-based authorization with
Apache Ranger • Uses metadata,
ODBC driver, and SQL syntax from
Apache Hive. In early 2013, a
column-oriented file format called
Parquet was announced for architectures including Impala. In December 2013,
Amazon Web Services announced support for Impala. In early 2014,
MapR added support for Impala. In 2015, another format called
Kudu was announced, which
Cloudera proposed to donate to the
Apache Software Foundation along with Impala. Impala graduated to an Apache Top-Level Project (TLP) on 28 November 2017. ==See also==