What is the Difference Between the Beehive and the Impala? with Proper Definition and Brief Explanation

The main difference between Hive and Impala is that the Hive is a data warehouse software that can be used to access and manage large distributed data sets built on Hadoop while Impala is a massively parallel SQL processing engine to manage and analyze data stored in Hadoop. the Beehive and the Impala

Hive is an open source data warehouse for querying and analyzing large data sets stored in Hadoop files. Impala provides the fastest way to access data stored in the Hadoop Distributed File System. Both are sub tools related to Hadoop.

Key Areas Covered                                  the Beehive and the Impala

1. What is Hadoop?
     – Definition, Functionality
2. What is hive?
     – Definition, Functionality
3. What is impala?
     – Definition, Functionality
4. What is the difference between hive and impala?
     – Comparison of key differences

Key terms

Big Data, Data Warehouse, Hadoop, Hive, Impala

What is Hadoop

Big data refers to a large set of data that has a high volume, velocity, and variety of data. Big data is collected daily and cannot be processed using traditional methods. Therefore, the Apache Software Foundation introduced a framework called Hadoop to manage and process big data. This is an open source framework.

Hadoop consists of two modules: MapReduce and Hadoop Distributed File System (HDFS). The MapReduce module helps process massive structured, semi-structured, and unstructured data on large groups of commodity hardware. Additionally, HDFS is used to store and process data sets. Provides a fault-tolerant file system to run on commodity hardware.

What is the hive

The Hadoop ecosystem consists of several child tools that help the Hadoop module. The hive is one of them. It was initially developed by Facebook but was later taken over by the Apache Software Foundation. It helps to summarize big data, query and analyze it easily. Provides SQL-like language for writing queries called Hive QL or HQL.

The process of Hadoop interaction with the Hadoop framework is as follows.

  1. Hive interface sends the query to units like JDBC, ODBC to execute the query.
  2. The unit then gets help from the query compiler to parse the query to check the syntax.
  3. The compiler then sends a metadata request to metastore.
  4. In return, the metastore sends the metadata to the compiler as a response.
  5. The compiler then checks the requirement and sends the plan back to the driver. Up to this point, the query parsing and compilation is complete.
  6. The unit then sends the execution plan to the execution engine.
  7. The job then runs. It is a MapReduce job. The runtime can execute metadata operations against the metastore.
  8. And, the results are achieved. The execution engine gets results from the data nodes.
  9. Now the execution engine sends the results to the controller.
  10. Finally, the driver sends the results to the Hive interfaces.

What is impala

Impala is a massively parallel processing SQL query engine used to process a large volume of data stored in the Hadoop cluster. It is written in C++ and Java. Provides higher performance than Hive.

It provides scalability, flexibility, SQL support, and multi-user performance. It allows users to communicate with HDFS using an SQL-like query called HBase much faster. Also, it can read various file formats like Parquet and Avro. It uses metadata, SQL syntax (Hive SQL), ODBC driver, and Hive-like user interface. Provides a unified platform for batch or real-time queries.

Difference Between Beehive and Impala


Hive is a data warehouse software project built on top of Apache Hadoop to provide data query and analysis. Impala is an open source bulk processing SQL query engine for data stored on a computer cluster running Apache Hadoop. Thus, this explains the fundamental difference between Hive and Impala.


The base of operation is another difference between Hive and Impala. Hive is based on the MapReduce algorithm. Impala is not based on the MapReduce algorithm. It implements a distributed architecture based on daemon processes. It also handles query execution running on the same machines.

Intermediate Results

Additionally, Hive materializes all intermediate results for you to improve scalability and fault tolerance. Impala streams intermediate results between executors.

Interactive Computing

Therefore, Impala is better for interactive computing than Hive.


Also, Impala is faster than Hive because it reduces latency. This is one big difference between Hive and Impala.

Type                                                            the Beehive and the Impala

Another difference between Hive and Impala is that Hive is a batch-based Hadoop MapReduce while Impala is a massively parallel processing SQL query engine.

Execution of Queries

Also, in Hive, the query output occurs because it is fault tolerant while a data node crashes during execution. In Impala, query execution starts from the beginning, while a data node is dropped during execution.

Complex Types

Hive supports complex types while Impala does not support complex types.


The difference between Hive and Impala is that Hive is a data warehouse software that can be used to access and manage large distributed data sets built on Hadoop while Impala is a massively parallel processing SQL engine for managing and analyzing stored data. in Hadoop..


1. “Hive – Introduction.” Www.tutorialspoint.com, Tutorials Point, available here.
2. “Impala Walkthrough.” Parallax Scrolling, Java Cryptography, YAML, Python Data Science, Java i18n, GitLab, TestRail, VersionOne, DBUtils, Common CLI, Seaborn, Ansible, LOLCODE, Current 2018, Apache Commons Collections, Available here.

Courtesy image:

1. “Apache Hive logo” By Davod – Own work, using file: Apache Hive logo.jpg as a base (Apache 2.0 License) via Commons Wikimedia.

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