DBMS > Sadas Engine vs. SWC-DB vs. Ultipa

System Properties Comparison Sadas Engine vs. SWC-DB vs. Ultipa

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Editorial information provided by DB-Engines

Name

Description

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A high performance, scalable Wide Column DBMS

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Relational DBMS

Wide column store

Graph DBMS

Secondary database models

Time Series DBMS

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

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Score	0.13
Rank	#335	Overall
	#31	Graph DBMS

Website

www.sadasengine.com

github.com/kashirin-alex/swc-db
www.swcdb.org

www.ultipa.com

Technical documentation

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

www.ultipa.com/document

Developer

SADAS s.r.l.

Alex Kashirin

Ultipa

Initial release

2006

2020

2019

Current release

8.0

0.5, April 2021

License

Commercial or Open Source

commercial

free trial version available

Open Source

GPL V3

commercial

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.

Implementation language

C++

Server operating systems

AIX
Linux
Windows

Linux

Data scheme

yes

schema-free

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

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
Proprietary protocol

Proprietary protocol
Thrift

RESTful HTTP API

Supported programming languages

.Net
C
C#
C++
Groovy
Java
PHP
Python

C++

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

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

managed by 'Learn by Usage'

User concepts

Access control

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

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