DBMS > IBM Cloudant vs. Microsoft Access vs. Microsoft SQL Server vs. RisingWave vs. Solr

System Properties Comparison IBM Cloudant vs. Microsoft Access vs. Microsoft SQL Server vs. RisingWave 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.)

Microsofts flagship relational DBMS

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

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

Primary database model

Document store

Relational DBMS

Search engine

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.64
Rank	#238	Overall
	#110	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.ibm.com/products/cloudant

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

www.microsoft.com/en-us/sql-server

www.risingwave.com/database

solr.apache.org

Technical documentation

cloud.ibm.com/docs/Cloudant

developer.microsoft.com/en-us/access

learn.microsoft.com/en-US/sql/sql-server

docs.risingwave.com/docs/current/intro

solr.apache.org/resources.html

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

RisingWave Labs

Apache Software Foundation

Initial release

2010

1992

1989

2022

2006

Current release

1902 (16.0.11328.20222), March 2019

SQL Server 2022, November 2022

1.2, September 2023

9.6.1, May 2024

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

commercial

restricted free version is available

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Erlang

C++

Rust

Java

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

Linux
Windows

Docker
Linux
macOS

All OS with a Java VM

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

Data scheme

schema-free

yes

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

Typing

predefined data types such as float or date

yes

Standard SQL-types and JSON

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 search fields are automatically indexed

SQL

Support of SQL

yes

but not compliant to any SQL standard

yes

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
PostgreSQL wire protocol

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Go
Java
JavaScript (Node.js)
Python

.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

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

UDFs in Python or Java

Java plugins

Triggers

yes

since Access 2010 using the ACE-engine

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

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

ACID

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

fine grained access rights according to SQL-standard

Users and Roles

yes

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