DBMS > Amazon Redshift vs. BigObject vs. HBase vs. IBM Cloudant vs. Microsoft Azure Cosmos DB

System Properties Comparison Amazon Redshift vs. BigObject vs. HBase vs. IBM Cloudant vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for real-time computations and queries

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

Database as a Service offering based on Apache CouchDB

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Wide column store

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

aws.amazon.com/redshift

bigobject.io

hbase.apache.org

www.ibm.com/products/cloudant

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

hbase.apache.org/book.html

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

Initial release

2012

2015

2008

2010

2014

Current release

2.3.4, January 2021

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache version 2

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

Erlang

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
Unix
Windows

using Cygwin

hosted

Data scheme

yes

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

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

restricted

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

RESTful HTTP/JSON API

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

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Groovy
Java
PHP
Python
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

yes

Coprocessors in Java

View functions (Map-Reduce) in JavaScript

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

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

none

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

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

atomic operations within a document possible

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

fine grained access rights according to SQL-standard

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

Access rights for users can be defined per database

Access rights can be defined down to the item level

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