DBMS > Drizzle vs. Faircom EDGE vs. Hazelcast vs. Snowflake

System Properties Comparison Drizzle vs. Faircom EDGE vs. Hazelcast 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.

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

A widely adopted in-memory data grid

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

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

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.faircom.com/products/faircom-edge

hazelcast.com

www.snowflake.com

Technical documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

hazelcast.org/imdg/docs

docs.snowflake.net/manuals/index.html

Developer

Drizzle project, originally started by Brian Aker

FairCom Corporation

Hazelcast

Snowflake Computing Inc.

Initial release

2008

1979

2008

2014

Current release

7.2.4, September 2012

V3, October 2020

5.3.6, November 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Restricted, free version available

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

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Implementation language

C++

ANSI C, C++

Java

Server operating systems

FreeBSD
Linux
OS X

Android
Linux

ARM, x86
Raspbian
Windows

All OS with a Java VM

hosted

Data scheme

yes

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

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

ANSI SQL queries

SQL-like query language

yes

APIs and other access methods

JDBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Event Listeners, Executor Services

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes

Events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

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
Tunable Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Role-based access control

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

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