DBMS > Drizzle vs. EXASOL vs. Kinetica vs. Snowflake

System Properties Comparison Drizzle vs. EXASOL vs. Kinetica vs. Snowflake

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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.

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

Fully vectorized database across both GPUs and CPUs

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

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

www.exasol.com

www.kinetica.com

www.snowflake.com

Technical documentation

www.exasol.com/resources

docs.kinetica.com

docs.snowflake.net/manuals/index.html

Developer

Drizzle project, originally started by Brian Aker

Exasol

Kinetica

Snowflake Computing Inc.

Initial release

2008

2000

2012

2014

Current release

7.2.4, September 2012

7.1, August 2021

License

Commercial or Open Source

Open Source

GNU GPL

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

C++

C, C++

Server operating systems

FreeBSD
Linux
OS X

Linux

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

with proprietary extensions

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

Java
Lua
Python
R

C++
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
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

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

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

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

Access rights for users and roles on table level

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

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