DBMS > Brytlyt vs. Cachelot.io vs. CrateDB vs. RDF4J vs. Snowflake

System Properties Comparison Brytlyt vs. Cachelot.io vs. CrateDB vs. RDF4J vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

In-memory caching system

Distributed Database based on Lucene

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

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

Primary database model

Relational DBMS

Key-value store

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

RDF store

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

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

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

Technical documentation

docs.brytlyt.io

cratedb.com/docs

rdf4j.org/documentation

docs.snowflake.net/manuals/index.html

Developer

Brytlyt

Crate

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Snowflake Computing Inc.

Initial release

2016

2015

2013

2004

2014

Current release

5.0, August 2023

License

Commercial or Open Source

commercial

Open Source

Simplified BSD License

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C, C++ and CUDA

C++

Java

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
OS X
Unix
Windows

hosted

Data scheme

yes

schema-free

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

yes

RDF Schemas

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

specific XML-type available, but no XML query functionality.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Memcached protocol

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

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

.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
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

user defined functions (Javascript)

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Configurable replication on table/partition-level

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

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

ACID

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

rights management via user accounts

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...
» more

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 >
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.