DBMS > Kinetica vs. Microsoft Azure Cosmos DB vs. MonetDB vs. SWC-DB vs. Transbase

System Properties Comparison Kinetica vs. Microsoft Azure Cosmos DB vs. MonetDB vs. SWC-DB vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

A relational database management system that stores data in columns

A high performance, scalable Wide Column DBMS

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

Document store
Spatial DBMS

Time Series DBMS

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

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

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

www.kinetica.com

azure.microsoft.com/services/cosmos-db

www.monetdb.org

github.com/kashirin-alex/swc-db
www.swcdb.org

www.transaction.de/en/products/transbase.html

Technical documentation

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

www.monetdb.org/Documentation

www.transaction.de/en/products/transbase/features.html

Developer

Kinetica

Microsoft

MonetDB BV

Alex Kashirin

Transaction Software GmbH

Initial release

2012

2014

2004

2020

1987

Current release

7.1, August 2021

Dec2023 (11.49), December 2023

0.5, April 2021

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

GPL V3

commercial

free development license

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

C, C++

C++

C and C++

Server operating systems

Linux

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

SQL 2003 with some extensions

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Proprietary protocol
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

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

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C++

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

JavaScript

yes, in SQL, C, R

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

none

Source-replica replication available in experimental status

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

yes

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

GPU vRAM or System RAM

User concepts

Access control

Access rights for users and roles on table level

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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