DBMS > Amazon Redshift vs. Badger vs. Datomic vs. Faircom EDGE vs. RethinkDB

System Properties Comparison Amazon Redshift vs. Badger vs. Datomic vs. Faircom EDGE vs. RethinkDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Key-value store
Relational DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	0.03
Rank	#380	Overall
	#59	Key-value stores
	#157	Relational DBMS

Score	2.84
Rank	#106	Overall
	#19	Document stores

Website

aws.amazon.com/redshift

github.com/dgraph-io/badger

www.datomic.com

www.faircom.com/products/faircom-edge

rethinkdb.com

Technical documentation

docs.aws.amazon.com/redshift

godoc.org/github.com/dgraph-io/badger

docs.datomic.com

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

rethinkdb.com/docs

Developer

Amazon (based on PostgreSQL)

DGraph Labs

Cognitect

FairCom Corporation

The Linux Foundation

since July 2017

Initial release

2012

2017

2012

1979

2009

Current release

1.0.6735, June 2023

V3, October 2020

2.4.1, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

limited edition free

commercial

Restricted, free version available

Open Source

Apache Version 2

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

Java, Clojure

ANSI C, C++

C++

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

string, binary, float, bool, date, geometry

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

ANSI SQL queries

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

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

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

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

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

yes

.Net, JavaScript, C/C++

Triggers

By using transaction functions

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

File partitioning

Customizable business rules for partitioning

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes

Synchronous and asynchronous realtime replication based on transaction logs

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

none

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

when using SQL

Transaction concepts

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

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

MVCC based

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

yes

users and table-level permissions

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