DBMS > Amazon Redshift vs. Fauna vs. Hazelcast vs. KeyDB vs. Kinetica

System Properties Comparison Amazon Redshift vs. Fauna vs. Hazelcast vs. KeyDB vs. Kinetica

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

A widely adopted in-memory data grid

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Fully vectorized database across both GPUs and CPUs

Primary database model

Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS
Time Series DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	1.55
Rank	#151	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Website

aws.amazon.com/redshift

fauna.com

hazelcast.com

github.com/Snapchat/KeyDB
keydb.dev

www.kinetica.com

Technical documentation

docs.aws.amazon.com/redshift

docs.fauna.com

hazelcast.org/imdg/docs

docs.keydb.dev

docs.kinetica.com

Developer

Amazon (based on PostgreSQL)

Fauna, Inc.

Hazelcast

EQ Alpha Technology Ltd.

Kinetica

Initial release

2012

2014

2008

2019

2012

Current release

5.3.6, November 2023

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

BSD-3

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

Scala

Java

C++

C, C++

Server operating systems

hosted

All OS with a Java VM

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

Secondary indexes

restricted

yes

by using the Redis Search module

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

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

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

Event Listeners, Executor Services

Lua

user defined functions

Triggers

yes

Events

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

yes

Replicated Map

Multi-source replication
Source-replica replication

Source-replica replication

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

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

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

Optimistic locking, atomic execution of commands blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

fine grained access rights according to SQL-standard

Identity management, authentication, and access control

Role-based access control

simple password-based access control and ACL

Access rights for users and roles on table level

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