HBase vs Impala: Compare Which is Better

In our last HBase tutorial, we discussed HBase vs RDBMS. Today, we will see HBase vs Impala. There is always a question occurs that while we have HBase then why to choose Impala over HBase instead of simply using HBase.

So to clear this doubt, here is an article “HBase vs Impala: Feature-wise Comparison”. First, we will see the meaning of HBase & Impala. Also, we will see the uses of Impala and HBase. 

So, let’s start the difference between HBase vs Impala.

Difference Between HBase and Impala

Before we start discussing Difference between HBase and Impala, let’s see what are these two terms?

• What is HBase?

A very popular non-relational database on Hadoop which stores data in a column-oriented store model is HBase. In HBase, HDFS is used as data storage layer and to process data it uses MapReduce. Basically, HBase is a complete nonrelational database running on Hadoop.

Moreover, HBase uses SQL statements to submit queries while processing the data in tables on HDFS and also supports the concepts of databases, tables, and columns.

• What is Impala?

Apache Impala offers great flexibility to query data in HBase tables. Moreover, for bulk loads and full-table-scan queries, Impala tables process data files stored on HDF great; although, by performing individual row or range lookups, HBase can perform efficient data processing.

More specifically, Impala considers HBase a key-value store where a key is mapped to one column in the Impala table whereas value fields are mapped to other columns.

Confused between HBase and Impala?

If our data is already stored in HBase but we want to use SQL requests since it is not possible as such with HBase, or even if we desire to join data from an HBase table with data from a MySQL table its one solution is, we can use Impala over HBase.

HBase vs Impala: Feature-wise Comparison

Below we are discussing the feature wise difference of HBase vs Impala:

i. Primary Database Model

• HBase

Primary database model of HBase is Wide column store. Basically, with the ability to hold very large numbers of dynamic columns, Wide column stores store data in records. It is also known as extensible record stores

• Impala

Whereas, primary database model of Impala is Relational DBMS. Basically, RDBMS support the relational data model and by the table name and a fixed number of attributes with fixed data types, its schema is defined.

ii. Developer

• HBase

Originally HBase was developed by Powerset, but now it is Apache’s top-level project.

• Impala

Whereas, Impala is developed by Cloudera.

iii. Initial Release

• HBase

Initially, it was released in the year 2008.

• Impala

In the year 2013, Impala was released.

iv. Current Release

• HBase

The current release of HBase is 1.4.3, in the year 2018, April.

• Impala

And, the current release of Apache Impala is 2.10.0.

v. License Info

• HBase

Under Apache version 2, HBase is Open Source.

• Impala

Similarly, Apache Impala is also open source, under Apache version 2.

vi. Implementation Language

• HBase

The implementation language of HBase is Java.

• Impala

Whereas, implementation language of  Impala is C++.

vii. Server Operating Systems

• HBase

There are some server operating systems of HBase are Linux, Unix, Windows

• Impala

And, for Impala, Linux is one server operating system only.

viii. Support of SQL

• HBase

There is no support of SQL in HBase.

• Impala

But, in Impala, SQL supports.

ix. APIs and Other Access Methods

• HBase

HBase offers several APIs, such as Java API, RESTful HTTP API, and Thrift.

• Impala

Whereas, Impala offers APIs as JDBC and ODBC.

x. Supported Programming Languages

• HBase

HBase supports various languages such as C, C#, C++, Groovy, Java, PHP, Python, and Scala.

• Impala

And, Impala supports all languages supporting JDBC/ODBC.

xi. Partitioning Methods

• HBase

Apache HBase supports Sharding method for storing different data on different nodes.

• Impala

Similarly, Impala also supports Sharding method for storing different data on different nodes.

xii. Consistency Concepts

• HBase

HBase supports Immediate Consistency.

• Impala

It supports Eventual Consistency.

Uses – HBase vs Impala

Although both HBase and Impala are parts of the Hadoop ecosystem, their functions and use cases vary:

HBase:

HBase is a distributed NoSQL database that uses column families and works on top of Hadoop and HDFS. It is made for real-time read and write access to huge amounts of structured and semi-structured data.
Use Cases: HBase is appropriate in situations where you want quick read and write access to large volumes of data, particularly time-series data, sensor data, logs, social media data, and applications that demand low-latency access, such real-time analytics and recommendation systems.

Impala:

Impala is a solution for managing analytical databases that is intended for interactive SQL queries on Hadoop data. For ad hoc SQL queries, it offers quick, in-memory, and parallel query processing.
Cases of Use Impala is the best tool for conducting intricate SQL queries and doing real-time data analysis on huge datasets kept in HDFS. Data exploration, reporting, business intelligence, and data visualisation tasks all frequently make use of it.

In conclusion, Impala is best suited for interactive SQL queries and data analysis on massive datasets, whereas HBase is best suited for real-time, low-latency, and high-volume data storage. You may utilise one or both of these tools in your Hadoop-based data processing and analytics processes, depending on the precise needs of your use case.

Conclusion

Hence, in this HBase vs Impala tutorial, we have seen the complete feature-wise Comparison on HBase vs Impala. However, to learn deeply about them, you can also refer relevant links given in blog to understand well. Still, if any doubt, ask in the comment tab.