DBMS > Amazon DynamoDB vs. Atos Standard Common Repository vs. Databricks vs. Fauna vs. H2

System Properties Comparison Amazon DynamoDB vs. Atos Standard Common Repository vs. Databricks vs. Fauna vs. H2

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

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Primary database model

Document store
Key-value store

Document store
Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial 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	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

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

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Website

aws.amazon.com/dynamodb

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

www.databricks.com

fauna.com

www.h2database.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.databricks.com

docs.fauna.com

www.h2database.com/html/main.html

Developer

Amazon

Atos Convergence Creators

Databricks

Fauna, Inc.

Thomas Mueller

Initial release

2012

2016

2013

2014

2005

Current release

1703

2.2.220, July 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

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

Scala

Java

Server operating systems

hosted

Linux

hosted

All OS with a Java VM

Data scheme

schema-free

Schema and schema-less with LDAP views

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

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

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

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

APIs and other access methods

RESTful HTTP API

LDAP

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

JDBC
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

All languages with LDAP bindings

Python
R
Scala

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

Java

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

Java Stored Procedures and User-Defined Functions

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

horizontal partitioning

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

With clustering: 2 database servers on different computers operate on identical copies of a database

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

Foreign keys

Referential integrity

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

yes, multi-version concurrency control (MVCC)

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

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

LDAP bind authentication

Identity management, authentication, and access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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