DBMS > DataFS vs. FatDB vs. ScyllaDB vs. Spark SQL

System Properties Comparison DataFS vs. FatDB vs. ScyllaDB vs. Spark SQL

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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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Cassandra and DynamoDB compatible wide column store

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Object oriented DBMS

Document store
Key-value store

Wide column store

Relational DBMS

Secondary database models

Graph DBMS

Key-value store

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

newdatabase.com

www.scylladb.com

spark.apache.org/sql

Technical documentation

dev.mobiland.com/Overview.xsp

docs.scylladb.com

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Mobiland AG

FatCloud

ScyllaDB

Apache Software Foundation

Initial release

2018

2012

2015

2014

Current release

1.1.263, October 2022

ScyllaDB Open Source 5.4.1, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Open Source (AGPL), commercial license available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Scylla Cloud: Create real-time applications that run at global scale with Scylla Cloud, the industry’s most powerful NoSQL DBaaS

Implementation language

C++

Scala

Server operating systems

Windows

Linux

Linux
OS X
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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

cluster global secondary indices

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like DML and DDL statements (CQL)

SQL-like DML and DDL statements

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

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

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

yes, Lua

Triggers

no, except callback-events from server when changes happened

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

Concurrency

Support for concurrent manipulation of data

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

in-memory tables

User concepts

Access control

Windows-Profile

Can implement custom security layer via applications

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
» more

Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
» more

Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
» more

Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
» more

Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
» more

Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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