DBMS > Kinetica vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. NuoDB vs. RDF4J

System Properties Comparison Kinetica vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. NuoDB vs. RDF4J

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 Wide Column Store for rapid development using massive semi-structured datasets

NuoDB is a webscale distributed database that supports SQL and ACID transactions

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Relational DBMS

RDF store

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial 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	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.kinetica.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

www.3ds.com/nuodb-distributed-sql-database

rdf4j.org

Technical documentation

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

doc.nuodb.com

rdf4j.org/documentation

Developer

Kinetica

Microsoft

Dassault Systèmes

originally NuoDB, Inc.

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2012

2014

2012

2013

2004

Current release

7.1, August 2021

License

Commercial or Open Source

commercial

limited edition free

Open Source

Eclipse Distribution License (EDL), v1.0.

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++

Java

Server operating systems

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

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

RESTful HTTP API

ADO.NET
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

JavaScript

Java, SQL

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

Implicit feature of the cloud service

data is dynamically stored/cached on the nodes where it is read/written

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

Managed transparently by NuoDB

none

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

optimistic locking

ACID

tunable commit protocol

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

yes

Temporary table

User concepts

Access control

Access rights for users and roles on table level

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

Standard SQL roles/ privileges, Administrative Users

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