Apache Hive is an effective standard for SQL-in-Hadoop. Hive is a front end for parsing SQL statements, generating logical plans, optimizing logical plans, translating them into physical plans which are executed by MapReduce jobs. Apache Hive is designed for the data warehouse system to ease the processing of adhoc queries on massive data sets stored in HDFS and ease data aggregations.
Impala is an open source SQL query engine developed after Google Dremel. Cloudera Impala is an SQL engine for processing the data stored in HBase and HDFS. Impala uses Hive megastore and can query the Hive tables directly. Unlike Hive, Impala does not translate the queries into MapReduce jobs but executes them natively.
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However, both Apache Hive and Cloudera Impala support the common standard HiveQL.
Hive vs. Impala
- Hive is slow but undoubtedly a great option for heavy ETL tasks where reliability plays a vital role, for instance the hourly log aggregations for advertising organizations. Impala is an open source SQL engine that can be used effectively for processing queries on huge volumes of data. Impala is faster and handles bigger volumes of data than Hive query engine.
- Query expressions in Hive are generated during compile time whereas Impala generates run time code for big loops through LLVM that helps in optimizing the code.
- Hive translates queries to be executed into MapReduce jobs under the hood involving overheads initially whereas Impala responds quickly through massively parallel processing.
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Impala is faster than Apache Hive but that does not mean that it is the one stop SQL solution for all big data problems. Impala is memory intensive and does not run effectively for heavy data operations like joins because it is not possible to push in everything into the memory. This is when Hive comes to the rescue. If an application has batch processing kind of needs over big data then organizations must opt for Hive. If they need real time processing of ad-hoc queries on subset of data then Impala is a better choice.