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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.
Impala provides low latency and high concurrency for BI/analytic queries on the Hadoop ecosystem, including Iceberg, open data formats, and most cloud storage options. Impala also scales linearly, even in multitenant environments.
Impala makes use of many familiar components within the Hadoop ecosystem. Impala can interchange data with other Hadoop components, as both a consumer and a producer, so it can fit in flexible ways into your ETL and ELT pipelines.
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 user interface (Impala query UI in Hue) as Apache Hive.
Oct 24, 2012 · A high-level architectural view is below: There are many advantages to this approach over alternative approaches for querying Hadoop data, including:: Thanks to local processing on data nodes, network bottlenecks are avoided. A single, open, and unified metadata store can be utilized.
Support for the most commonly-used Hadoop file formats, including the Apache Parquet project. Apache-licensed, 100% open source. More about Impala. To learn more about Impala as a business user, or to try Impala live or in a VM, please visit the Impala homepage.
Sep 2, 2022 · Apache Impala combines the flexibility and scalability of Hadoop with the SQL support and multi-user performance of a traditional analytics database using components such as HDFS, Meta store, HBase, Sentry, and YARN.
