DBMS > Datomic vs. EXASOL vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Snowflake

System Properties Comparison Datomic vs. EXASOL vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	1.66
Rank	#138	Overall
	#64	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

www.datomic.com

www.exasol.com

www.kinetica.com

azure.microsoft.com/services/cosmos-db

www.snowflake.com

Technical documentation

docs.datomic.com

www.exasol.com/resources

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

docs.snowflake.net/manuals/index.html

Developer

Cognitect

Exasol

Kinetica

Microsoft

Snowflake Computing Inc.

Initial release

2012

2000

2012

2014

Current release

1.0.7180, July 2024

7.1, August 2021

License

Commercial or Open Source

commercial

limited edition free

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, Clojure

C, C++

Server operating systems

All OS with a Java VM

Linux

hosted

Data scheme

yes

schema-free

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

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
RESTful HTTP API

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

CLI Client
JDBC
ODBC

Supported programming languages

Clojure
Java

Java
Lua
Python
R

C++
Java
JavaScript (Node.js)
Python

.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

yes

Transaction Functions

user defined functions

JavaScript

user defined functions

Triggers

By using transaction functions

yes

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

JavaScript

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

GPU vRAM or System RAM

User concepts

Access control

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

Access rights for users and roles on table level

Access rights can be defined down to the item level

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

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