DBMS > Amazon DynamoDB vs. Atos Standard Common Repository vs. Datomic vs. Faircom EDGE vs. Fauna

System Properties Comparison Amazon DynamoDB vs. Atos Standard Common Repository vs. Datomic vs. Faircom EDGE vs. Fauna

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

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.

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store
Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

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

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Website

aws.amazon.com/dynamodb

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.datomic.com

www.faircom.com/products/faircom-edge

fauna.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.datomic.com

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

docs.fauna.com

Developer

Amazon

Atos Convergence Creators

Cognitect

FairCom Corporation

Fauna, Inc.

Initial release

2012

2016

2012

1979

2014

Current release

1703

1.0.6735, June 2023

V3, October 2020

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

limited edition free

commercial

Restricted, free version 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

Java

Java, Clojure

ANSI C, C++

Scala

Server operating systems

hosted

Linux

All OS with a Java VM

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Data scheme

schema-free

Schema and schema-less with LDAP views

yes

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

schema-free

Typing

predefined data types such as float or date

yes

optional

yes

yes, ANSI Standard SQL Types

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

yes

SQL

Support of SQL

yes

ANSI SQL queries

APIs and other access methods

RESTful HTTP API

LDAP

RESTful HTTP API

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

RESTful HTTP API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

All languages with LDAP bindings

Clojure
Java

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

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

.Net, JavaScript, C/C++

user defined functions

Triggers

yes

by integration with AWS Lambda

yes

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

But extensive use of caching in the application peers

File partitioning

Customizable business rules for partitioning

horizontal partitioning

consistent hashing

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

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency
Tunable Consistency

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

ACID across one or more tables within a single AWS account and region

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

LDAP bind authentication

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

Identity management, authentication, and access control

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