DBMS > EXASOL vs. Kdb vs. Microsoft Azure Cosmos DB vs. SiteWhere vs. Snowflake

System Properties Comparison EXASOL vs. Kdb vs. Microsoft Azure Cosmos DB vs. SiteWhere vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

High performance Time Series DBMS

Globally distributed, horizontally scalable, multi-model database service

M2M integration platform for persisting/querying time series data

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Time Series DBMS
Vector DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

Spatial DBMS

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

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	7.70
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.06
Rank	#367	Overall
	#36	Time Series DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

www.exasol.com

kx.com

azure.microsoft.com/services/cosmos-db

github.com/sitewhere/sitewhere

www.snowflake.com

Technical documentation

www.exasol.com/resources

code.kx.com

learn.microsoft.com/azure/cosmos-db

sitewhere1.sitewhere.io/index.html

docs.snowflake.net/manuals/index.html

Developer

Exasol

Kx Systems, a division of First Derivatives plc

Microsoft

SiteWhere

Snowflake Computing Inc.

Initial release

2000

kdb was released 2000, kdb+ in 2003

2014

2010

2014

Current release

3.6, May 2018

License

Commercial or Open Source

commercial

free 32-bit version

commercial

Open Source

Common Public Attribution License Version 1.0

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

Java

Server operating systems

Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

predefined scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes

SQL-like query language (q)

SQL-like query language

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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

HTTP REST

CLI Client
JDBC
ODBC

Supported programming languages

Java
Lua
Python
R

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

JavaScript

user defined functions

Triggers

yes

with views

JavaScript

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding

based on HBase

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

selectable replication factor

based on HBase

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

Immediate Consistency

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

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

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

rights management via user accounts

Access rights can be defined down to the item level

Users with fine-grained authorization concept

Users with fine-grained authorization concept, user roles and pluggable authentication

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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