DBMS > Brytlyt vs. Coveo vs. Hazelcast vs. Snowflake

System Properties Comparison Brytlyt vs. Coveo vs. Hazelcast vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

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

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

A widely adopted in-memory data grid

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

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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Trend Chart

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

Technical documentation

docs.brytlyt.io

docs.coveo.com

hazelcast.org/imdg/docs

docs.snowflake.net/manuals/index.html

Developer

Brytlyt

Coveo

Hazelcast

Snowflake Computing Inc.

Initial release

2016

2012

2008

2014

Current release

5.0, August 2023

5.3.6, November 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

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++ and CUDA

Java

Server operating systems

Linux
OS X
Windows

hosted

All OS with a Java VM

hosted

Data scheme

yes

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

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

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

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

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

RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C#
Java
JavaScript
Python

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

yes

Event Listeners, Executor Services

user defined functions

Triggers

yes

Events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Replicated Map

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

one or two-phase-commit; repeatable reads; read commited

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

fine grained access rights according to SQL-standard

granular access controls, API key management, content filters

Role-based access control

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

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