DBMS > IBM Cloudant vs. Microsoft Access vs. Microsoft Azure Cosmos DB vs. Newts vs. Solr

System Properties Comparison IBM Cloudant vs. Microsoft Access vs. Microsoft Azure Cosmos DB vs. Newts vs. Solr

Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Globally distributed, horizontally scalable, multi-model database service

Time Series DBMS based on Cassandra

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.ibm.com/products/cloudant

www.microsoft.com/en-us/microsoft-365/access

azure.microsoft.com/services/cosmos-db

opennms.github.io/newts

solr.apache.org

Technical documentation

cloud.ibm.com/docs/Cloudant

developer.microsoft.com/en-us/access

learn.microsoft.com/azure/cosmos-db

github.com/OpenNMS/newts/wiki

solr.apache.org/resources.html

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

OpenNMS Group

Apache Software Foundation

Initial release

2010

1992

2014

2006

Current release

1902 (16.0.11328.20222), March 2019

9.6.0, April 2024

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

C++

Java

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

hosted

Linux
OS X
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

JSON types

yes

supports customizable data types and automatic typing

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

All properties auto-indexed by default

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

but not compliant to any SQL standard

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

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

HTTP REST
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

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

Java

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

yes

since Access 2010 using the ACE-engine

JavaScript

Java plugins

Triggers

yes

since Access 2010 using the ACE-engine

JavaScript

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes

Implicit feature of the cloud service

selectable replication factor

based on Cassandra

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a document possible

ACID

but no files for transaction logging

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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 rights for users can be defined per database

a simple user-level security was built in till version Access 2003

Access rights can be defined down to the item level

yes

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