DBMS > HBase vs. IBM Cloudant vs. IBM Db2 vs. Microsoft Azure Cosmos DB

System Properties Comparison HBase vs. IBM Cloudant vs. IBM Db2 vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

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

Database as a Service offering based on Apache CouchDB

Common in IBM host environments, 2 different versions for host and Windows/Linux

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Wide column store

Document store

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Spatial DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

hbase.apache.org

www.ibm.com/products/cloudant

www.ibm.com/products/db2

azure.microsoft.com/services/cosmos-db

Technical documentation

hbase.apache.org/book.html

cloud.ibm.com/docs/Cloudant

www.ibm.com/docs/en/db2

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

IBM

Microsoft

Initial release

2008

2010

1983

host version

2014

Current release

2.3.4, January 2021

12.1, October 2016

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Erlang

C and C++

Server operating systems

Linux
Unix
Windows

using Cygwin

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

hosted

Data scheme

schema-free, schema definition possible

schema-free

yes

schema-free

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

JSON types

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
Thrift

RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

yes

Coprocessors in Java

View functions (Map-Reduce) in JavaScript

yes

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

with separate tools (MQ, InfoSphere)

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

atomic operations within a document possible

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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