DBMS > Drizzle vs. Kinetica vs. OmniSci

System Properties Comparison Drizzle vs. Kinetica vs. OmniSci

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

GPU-accelerated database for real-time analysis of large and streaming datasets

A high performance, in-memory, column-oriented RDBMS, designed to run on GPUs

Primary database model

Relational DBMS

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

Score	0.74
Rank	#195	Overall
	#97	Relational DBMS

Score	1.51
Rank	#135	Overall
	#69	Relational DBMS

Website

www.kinetica.com

www.omnisci.com

Technical documentation

www.kinetica.com/docs

docs.omnisci.com

Developer

Drizzle project, originally started by Brian Aker

Kinetica

MapD Technologies, Inc.

Initial release

2008

2012

2016

Current release

7.2.4, September 2012

6.0

5.5, January 2021

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache Version 2; enterprise edition available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C, C++

C++ and CUDA

Server operating systems

FreeBSD
Linux
OS X

Linux

Data scheme

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Thrift

Supported programming languages

C
C++
Java
PHP

C++
Java
JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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Recent citations in the news

Global GPU Database Market 2025: Kinetica, Omnisci, Sqream, Neo4j, Nvidia, Brytlyt, Jedox, Blazegraph, Blazingdb, Zilliz, Heterodb, H2o.Ai, Fastdata.Io, Fuzzy Logix, Graphistry, Anaconda
28 February 2021, NY Market Reports

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26 February 2021, The Bisouv Network

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26 February 2021, The Bisouv Network

2021: Top 5 Disruptive Trends in the GPU Database Market by Top Companies : Kinetica, OmniSci, SQream, Neo4j, NVIDIA, Brytlyt, Blazegraph, BlazingDB, Zilliz, and Jedox. Moreover, the other prospective players in the GPU database market are HeteroDB, H2O.ai, FASTDATA.io, Fuzzy Logix, and Anaconda.
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