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Difference between Impala and HBase 
Uses – HBase vs Impala
Use of HBase
Use of Impala
Key Takeaways
Last Updated: Mar 27, 2024

HBase vs Impala

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There is always the issue of why, since we have HBase, we chose Impala over HBase rather than simply using HBase. 

If our data is already in HBase but we want to use SQL queries since this is not feasible with HBase, or if we want to link data from an HBase database with data from a MySQL table, we may use Impala over HBase.


  • Impala is a Hadoop-based query engine.
  • It enables high-performance, low-latency SQL queries on Hadoop data.
  • It is a free and open-source piece of software. It allows for in-memory data processing.
  • It is a forerunner in using the Parquet file format, a columnar storage architecture intended for large-scale queries common in data warehouse applications.
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  • This concept gives users random access to enormous volumes of structured data.
  • It is column-oriented and built on top of the Hadoop file system. 
  • The data was previously stored in HDFS. 
  • It is a free and open-source database that allows for data replication.

Difference between Impala and HBase 





Primary Database Model

Wide column store is HBase's primary database model. Wide column stores store data in records by carrying a high number of dynamic columns. Extensible record storage is another name for it.

Impala's primary database model is Relational DBMS. RDBMS supports the relational data model, and its schema is specified by the table name and a given amount of attributes with fixed data types.



Powerset created HBase, but it is now an Apache top-level project.

Cloudera is the company behind Impala.

Initial Release

HBase was released in 2008

Impala was released in 2013


Current Release

HBase version 2.4.9 is the current recent stable release, which was released in December of 2021.

Impala version 4.0.0 is the current recent stable release, which was released in July of 2021.

License Info

HBase is open-source under Apache version 2

Impala is open-source under Apache version 2

Implementation Language

HBase is mainly written and implemented in Java

Impala is written and implemented in C++

Server Operating Systems

Linux, Unix, Windows

Only Linux

Support of SQL

No support for SQL

Supports SQL

APIs and Other Access Methods

HBase has a number of APIs, including a Java API, a RESTful HTTP API, and a Thrift API.

Impala provides JDBC and ODBC APIs.

Supported Programming Languages

HBase is compatible with many languages, including C, C#, C++, Groovy, Java, PHP, Python, and Scala.

Impala supports all languages that support JDBC or ODBC

Partitioning Methods

Apache HBase provides the sharding approach to store separate data on multiple nodes.

Impala also supports sharding, allowing distinct data to be stored on multiple nodes.

Consistency Concepts

HBase supports Immediate Consistency.

Impala supports Eventual Consistency.


Uses – HBase vs Impala

Use of HBase

  • We choose Apache HBase for random, real-time read/write access to Big Data.
  • With the aid of HBase, we can easily host massive tables on top of commodity hardware clusters.
  • HBase, like Google's Bigtable, is a non-relational database paradigm. To explain further, Bigtable operates on the Google File System, whereas HBase operates on top of Hadoop and HDFS.

Use of Impala

  • In simple words, Impala is built to work well with BI tools.
  • It also supports Standard ANSI SQL (92, with 2003 analytic extensions), UDFs/UDAs, correlated subqueries, nested types, and various other features.
  • Impala supports a variety of data types, including integer and floating-point types, STRING, CHAR, VARCHAR, and TIMESTAMP.


Must Read Apache Server


  1. Can Impala query HBase?
    Impala queries HBase data using the HBase client API via Java Native Interface (JNI). This querying does not directly read HFiles.
  2. What is Impala used for?
    Impala delivers large data analysis tools to data analysts for speedy trials and concept validation. Hive may be used to convert data first, and then Impala can be used to execute quick data analysis on the resultant data set processed by Hive. MapReduce is not a parallel computing framework.
  3. Why is Impala not Tolerant?
    Impala does not provide fault tolerance, although Apache Hive does. When a hive query is conducted, if the DataNode fails while the query is being executed, the query's result is provided since Hive is fault-tolerant. Impala, on the other hand, does not fall within this category.
  4. Is Impala a database?
    Impala is a programming language, not a database. Impala is a SQL query engine that uses MPP (Massive Parallel Processing). It is a SQL interface built on top of the HDFS framework.
  5. Why is HBase used?
    HBase is a non-relational column-oriented database management system that works on top of the Hadoop Distributed File System (HDFS). HBase provides a fault-tolerant method of storing sparse data sets, which are common in many big data applications. HBase does support the development of applications in Apache Avro, REST, and Thrift.

Key Takeaways

Cheers if you reached here! In this blog, We have seen the entire feature-by-feature comparison of HBase vs Impala.

Furthermore, we spoke about the differences in their uses.

On the other hand, learning never ceases, and there is always more to learn. So, keep learning and keep growing, ninjas!

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