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Difference between Impala and hBASE

Last Updated : 14 Mar, 2023
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1. Impala: Impala is a query engine that runs on Hadoop. It provides high-performance, low-latency SQL queries on data stored in Hadoop. It is open-source software. It supports in-memory data processing. It is pioneering the use of the Parquet file format, a columnar storage layout that is optimized for large-scale queries typical in data warehouse scenarios. 

2. HBase: This model is used to provide random access to a large amount of structured data. It builds on the top of the hadoop file system and is column-oriented in nature. It is used to store the data in HDFS. It is an open-source database that provides data replication. 

Similarities:

  1. Integration with the Hadoop ecosystem.
    Both Impala and hBASE are part of the Apache Hadoop ecosystem and are designed to work with HDFS. They leverage the distributed computing power of Hadoop and can be used alongside other Hadoop tools such as Hive, Pig, and MapReduce.
  2. Scalability
    Both Impala and hBASE are designed to scale horizontally, meaning that additional nodes can be added to the cluster to increase capacity and handle growing amounts of data. This makes them suitable for big data processing and storage.
  3. Distributed computing
    Both Impala and hBASE use a distributed computing architecture, with data distributed across multiple nodes in a cluster. This allows for parallel processing of queries and faster data retrieval.
  4. Open source
    Impala and hBASE are both open-source technologies that are freely available to the public. This allows for greater collaboration and innovation within the developer community.
  5. Fault tolerance
    Both Impala and hBASE are designed to be fault-tolerant, meaning that they can handle node failures without losing data. They use techniques such as replication and data sharding to ensure that data is always available and can be recovered in the event of a failure.

Difference between Impala and HBase:

Parameters Impala HBase
Basics Impala is analytic Database Management System (DBMS) for Hadoop. Wide-column database based on Apache Hadoop and BigTable concepts. 
Developed by It was developed by Cloudera. Developed by Apache Software Foundation.
Releasing year Impala was released in 2013. HBase was released in 2008.
Website www.cloudera.com/­products/­open-source/­apache-hadoop/­impala.html hbase.apache.org
Documentation docs.cloudera.com/­documentation/­enterprise/­latest/­topics/­impala.html hbase.apache.org
Implementation Language Impala is implemented using C++programming language. HBase is implemented using  JAVA programming language.
Server OS (Operating System) Linux is the only server operating system of Impala. Linux, Unix and Windows are server operating systems of HBase.
Primary Database Model It uses Relational Database Management System (RDBMS). It uses Column-oriented model.
Secondary Database Model It uses Document Store as Secondary Database Model. It does not use any Secondary Database Model.
SQL It supports SQL such as DML and DDL statements. It does not support SQL(Structured Query Language).
Triggers Triggers are not used in Impala. Triggers are used in HBase.
Supported Programming Languages All languages supporting JDBC/ODBC. C, C#, C++, Java, PHP, Python, Scala
APIs JDBC and ODBC are the APIs and access methods used in Impala. Java API, RESTful HTTP API, Thrift are the APIs and access methods used in Impala.
Replication methods Replication methods used in Impala are selectable replication factor. Replication methods used in HBase are Master-master replication, Master-slave replication.
Consistency  Eventual Consistency Immediate Consistency or Eventual Consistency
In-memory capabilities It does not support In-memory capabilities. It supports In-memory capabilities.
Uses
  • Impala works well with BI tools.
  • Inclusion of Standard ANSI SQL makes it possible to have features like UDFs/UDAs, correlated subqueries, nested types, and many more.
  • Impala supports a variety of data types, including integer and floating point types, STRING, CHAR, VARCHAR, and TIMESTAMP.
  • For BI-style queries
  • Quick Implementation
  • Enterprise-class security using authentication mechanism
  • In Partial data analyzation
  • Real time
  • Used for random, real-time read/write access to Big Data.
  • Helps in hosting very big tables on commodity hardware clusters.
  • Medical field
  • Sports
  • eCommerce
Key Customers
  • Nike
  • Citigroup
  • Facebook
  • Twitter
  • Yahoo

Conclusion: mpala and hBASE are both powerful technologies that are designed for different use cases. Impala provides fast query performance and support for SQL querying and BI tools, while hBASE provides fast data access and retrieval for unstructured or semi-structured data. 


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