DBMS > BigObject vs. Kdb vs. Microsoft Azure Cosmos DB vs. RDFox

System Properties Comparison BigObject vs. Kdb vs. Microsoft Azure Cosmos DB vs. RDFox

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

High performance Time Series DBMS

Globally distributed, horizontally scalable, multi-model database service

High performance knowledge graph and semantic reasoning engine

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS
Vector DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS
RDF store

Secondary database models

Relational DBMS

Spatial DBMS

Relational DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

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

Score	0.23
Rank	#308	Overall
	#25	Graph DBMS
	#14	RDF stores

Website

bigobject.io

kx.com

azure.microsoft.com/services/cosmos-db

www.oxfordsemantic.tech

Technical documentation

docs.bigobject.io

code.kx.com

learn.microsoft.com/azure/cosmos-db

docs.oxfordsemantic.tech

Developer

BigObject, Inc.

Kx Systems, a division of First Derivatives plc

Microsoft

Oxford Semantic Technologies

Initial release

2015

2000

kdb was released 2000, kdb+ in 2003

2014

2017

Current release

3.6, May 2018

6.0, Septermber 2022

License

Commercial or Open Source

commercial

free community edition available

commercial

free 32-bit version

commercial

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++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X
Solaris
Windows

hosted

Linux
macOS
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.

yes

Secondary indexes

yes

table attribute 'grouped'

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (q)

SQL-like query language

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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

RESTful HTTP API
SPARQL 1.1

Supported programming languages

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

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

C
Java

Server-side scripts

Stored procedures

Lua

user defined functions

JavaScript

Triggers

yes

with views

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

Implicit feature of the cloud service

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

rights management via user accounts

Access rights can be defined down to the item level

Roles, resources, and access types

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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