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/Âproducts/Âopen-source/Âapache-hadoop/Âimpala.html |
hbase.apache.org |
Documentation |
docs.cloudera.com/Âdocumentation/Âenterprise/Âlatest/Âtopics/Âimpala.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
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- 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
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Key Customers |
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