Impala is an open-source MPP (massively parallel processing) query engine that was designed to run interactive analytic queries against data stored in HDFS (Hadoop Distributed File System). The name Impala comes from the word “impacting” and indicates its ability to process data at high speed. Impala’s goal is to provide a fast and easy-to-use interface for data exploration.
Impala is designed to speed up queries on large amounts of distributed data, across many nodes. It is used to analyze and explore data at very high speeds, typically faster than Hive/MapReduce.
- Impala can be used to read data from any source. Impala supports many different data sources, including text files, sequence files, Parquet, Avro, RCFile, ORC, and Hive
- Impala works in a straightforward way. Unlike SQLite or MySQL databases, Impala makes it easy to get started
- The data is distributed across many nodes and the user does not need to sort or pre-aggregate the) Impala can help users make more informed and accurate decisions
- With it, companies can have better-prepared data for faster analysis
- Users can query data already in HDFS, which reduces the data transfer time
- Since Impala is embedded in the Big Data platform, there is no need to switch between different tools to store and analyze data
- Impala can also enable users to avoid the problem of schema-on-read. This is because Impala can use both existing and new schemas for data, depending on the needs of the user
- It is important to note that it can also be used with data stored in HBase, MapR-DB, and Amazon S3