DBMS > Apache HBase vs. GridGain vs. Microsoft Azure Table Storage

System Properties Comparison Apache HBase vs. GridGain vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

GridGain is an in-memory computing platform, built on Apache Ignite

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Wide column store

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Wide column store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	25.00
Rank	#26	Overall
	#2	Wide column stores

Score	1.37
Rank	#154	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#70	Relational DBMS

Score	3.05
Rank	#84	Overall
	#6	Wide column stores

Website

hbase.apache.org

www.gridgain.com

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

hbase.apache.org/book.html

www.gridgain.com/docs/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

GridGain Systems, Inc.

Microsoft

Initial release

2008

2007

2012

Current release

2.3.4, January 2021

GridGain 8.5.1

License

Commercial or Open Source

Open Source

Apache version 2

commercial, open source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, C++, .Net, Python, REST, SQL

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux
OS X
Solaris
Windows
z/OS

hosted

Data scheme

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

Java API
RESTful HTTP API
Thrift

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

RESTful HTTP API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes (replicated cache)

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Single row ACID (across millions of columns)

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Role-based access control
Security Hooks for custom implementations

Access rights based on private key authentication or shared access signatures

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