DBMS > CrateDB vs. EXASOL vs. MonetDB vs. Snowflake

System Properties Comparison CrateDB vs. EXASOL vs. MonetDB vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

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

A relational database management system that stores data in columns

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

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Spatial DBMS

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

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

Technical documentation

cratedb.com/docs

www.exasol.com/resources

www.monetdb.org/Documentation

docs.snowflake.net/manuals/index.html

Developer

Crate

Exasol

MonetDB BV

Snowflake Computing Inc.

Initial release

2013

2000

2004

2014

Current release

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

commercial

Open Source

Mozilla Public License 2.0

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.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

Java

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

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, but no triggers and constraints, and PostgreSQL compatibility

yes

SQL 2003 with some extensions

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

Java
Lua
Python
R

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions (Javascript)

user defined functions

yes, in SQL, C, R

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

none

Source-replica replication available in experimental status

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

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

User concepts

Access control

rights management via user accounts

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

fine grained access rights according to SQL-standard

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

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
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Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
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Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
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Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
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Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
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Licensing and pricing models

See CrateDB pricing >
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