DBMS > Datomic vs. HEAVY.AI vs. Kinetica vs. Microsoft SQL Server vs. Snowflake

System Properties Comparison Datomic vs. HEAVY.AI vs. Kinetica vs. Microsoft SQL Server 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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Fully vectorized database across both GPUs and CPUs

Microsofts flagship relational DBMS

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

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.datomic.com

github.com/heavyai/heavydb
www.heavy.ai

www.kinetica.com

www.microsoft.com/en-us/sql-server

www.snowflake.com

Technical documentation

docs.datomic.com

docs.heavy.ai

docs.kinetica.com

learn.microsoft.com/en-US/sql/sql-server

docs.snowflake.net/manuals/index.html

Developer

Cognitect

HEAVY.AI, Inc.

Kinetica

Microsoft

Snowflake Computing Inc.

Initial release

2012

2016

2012

1989

2014

Current release

1.0.7075, December 2023

5.10, January 2022

7.1, August 2021

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2; enterprise edition available

commercial

restricted free version is available

commercial

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, Clojure

C++ and CUDA

C, C++

C++

Server operating systems

All OS with a Java VM

Linux

Linux
Windows

hosted

Data scheme

yes

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

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

CLI Client
JDBC
ODBC

Supported programming languages

Clojure
Java

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

user defined functions

Triggers

By using transaction functions

yes

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

yes

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

Round robin

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication

Source-replica replication

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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

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