DBMS > FatDB vs. Galaxybase vs. Spark SQL

System Properties Comparison FatDB vs. Galaxybase 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

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

Scalable, ACID-compliant native distributed parallel graph platform

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

Primary database model

Document store
Key-value store

Graph DBMS

Relational DBMS

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

Score	0.00
Rank	#363	Overall
	#36	Graph DBMS

Score	23.12
Rank	#35	Overall
	#21	Relational DBMS

Website

galaxybase.com

spark.apache.org/sql

Technical documentation

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

Developer

FatCloud

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

Apache Software Foundation

Initial release

2012

2017

2014

Current release

Nov 20, November 2021

3.2.0, October 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

C and Java

Scala

Server operating systems

Windows

Linux

Linux
OS X
Windows

Data scheme

schema-free

Strong typed schema

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

SQL-like DML and DDL statements

APIs and other access methods

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

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

JDBC
ODBC

Supported programming languages

Go
Java
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

user defined procedures and functions

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

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

User concepts

Access control

Can implement custom security layer via applications

Role-based access control

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