DBMS > FatDB vs. Fauna vs. Splice Machine vs. Transwarp ArgoDB

System Properties Comparison FatDB vs. Fauna vs. Splice Machine vs. Transwarp ArgoDB

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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.

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Distributed Analytical Database to replace Hadoop+MPP hybrid architecture

Primary database model

Document store
Key-value store

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Search engine

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

Score	1.55
Rank	#143	Overall
	#26	Document stores
	#13	Graph DBMS
	#66	Relational DBMS
	#13	Time Series DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.04
Rank	#361	Overall
	#153	Relational DBMS

Website

fauna.com

splicemachine.com

www.transwarp.cn/en/product/argodb

Technical documentation

docs.fauna.com

splicemachine.com/how-it-works

docs.transwarp.cn/#/documents-support/docs?category=ARGODB

Developer

FatCloud

Fauna, Inc.

Splice Machine

Transwarp

Initial release

2012

2014

Current release

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

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

Server operating systems

Windows

hosted

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

Via inetgration in SQL Server

yes

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

user defined functions

yes

Java

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

consistent hashing

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Can implement custom security layer via applications

Identity management, authentication, and access control

Access rights for users, groups and roles according to SQL-standard

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