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A to Z Full Forms and Acronyms

Hive and Impala in Big Data-Big Data - (PART- 4)

In this article, we will discuss Hive and Impala

Hive and Impala

Hive and Impala provide an SQL-like interface for users to extract data from Hadoop system. They reside on top of Hadoop and can be used to query data from underlying storage components.

Hive and Impala: Similarities

Hive and Impala are similar in the following ways:

  • More productive than writing MapReduce or Spark directly.
  • Offers interoperability with other systems.
  • Brings large-scale data analysis to a broader audience.

Hive and Impala: Differences

Hive Impala
  • Hive was developed by Facebook in 2007.
  • Impala was developed by the Cloudera in 2012.
  • It is an open-source Apache project.
  • It is an incubation Apache project.
  • It uses HQL to query the structured data in a metastore.
  • It uses Impala SQL for ad hoc queries.
  • It is suitable for structured data.
  • It is designed for high concurrency and ad hoc queries.
  • It has a high-level abstraction layer on top of MapReduce and Apache Spark.
  • It has a high performance dedicated SQL engine.

Hive and Impala - Comparison


  • Hive is highly extensible.
  • It provides more features than Impala.
  • It is used mostly for batch processing.


  • Impala is used mainly for interactive queries and data analysis.
  • It accommodates many concurrent users.
  • It comprises a specialized SQL engine that offers 5 to 50 times faster performance than Hive.

Relational Database s vs Hive vs Impala

Features Relational Databases Hive Impala
Query Language SQL(Full) SQL(subset) SQL(subset)
Update individual records YES NO NO
Delete individual records YES NO NO


Index Supports Extensive Limited NO
Latency Low High Average
Data Size TB PB PB

Hive and Impala are commonly used to analyze social media coverage.

Executing a query in Hive and Impala


  • Parse HQL.
  • Make Optimizations.
  • Plan execution.
  • Submit job(s) to the cluster.
  • Monitor progress.
  • Process data using MapReduce or Apache Spark.
  • Store the data in HDFS.


  • Parse Impala SQL.
  • Make Optimizations.
  • Plan execution.
  • Execute query on the cluster.
  • Store the data in HDFS.


  • Hive and Impala are tools to perform SQL queries on data residing on HDFS or Hbase.
  • Hive and Impala can solve the Big Data problems but cannot replace a traditional RDBMS.
  • Hive runs MapReduce or Spark jobs on Hadoop based on HQL statements.
  • Impala uses a very fast specialized SQL engine that is faster than MapReduce.
A to Z Full Forms and Acronyms

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