DBMS > Apache Druid vs. Kinetica vs. Microsoft Azure Cosmos DB vs. SAP SQL Anywhere vs. SiteWhere

System Properties Comparison Apache Druid vs. Kinetica vs. Microsoft Azure Cosmos DB vs. SAP SQL Anywhere vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

druid.apache.org

www.kinetica.com

azure.microsoft.com/services/cosmos-db

www.sap.com/products/technology-platform/sql-anywhere.html

github.com/sitewhere/sitewhere

Technical documentation

druid.apache.org/docs/latest/design

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

help.sap.com/docs/SAP_SQL_Anywhere

sitewhere1.sitewhere.io/index.html

Developer

Apache Software Foundation and contributors

Kinetica

Microsoft

SAP

formerly Sybase

SiteWhere

Initial release

2012

2014

1992

2010

Current release

29.0.1, April 2024

7.1, August 2021

17, July 2015

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++

Java

Server operating systems

Linux
OS X
Unix

Linux

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

predefined scheme

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL for querying

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

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

ADO.NET
HTTP API
JDBC
ODBC

HTTP REST

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

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

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

Server-side scripts

Stored procedures

user defined functions

JavaScript

yes, in C/C++, Java, .Net or Perl

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

manual/auto, time-based

Sharding

Implicit feature of the cloud service

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Source-replica replication

yes

Implicit feature of the cloud service

Source-replica replication

Database mirroring

selectable replication factor

based on HBase

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

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

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

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

Users with fine-grained authorization concept

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