DBMS > Atos Standard Common Repository vs. Sadas Engine vs. Splice Machine vs. SQream DB

System Properties Comparison Atos Standard Common Repository vs. Sadas Engine vs. Splice Machine vs. SQream DB

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Document store
Key-value store

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

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

www.sadasengine.com

splicemachine.com

sqream.com

Technical documentation

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

splicemachine.com/how-it-works

docs.sqream.com

Developer

Atos Convergence Creators

SADAS s.r.l.

Splice Machine

SQream Technologies

Initial release

2016

2006

2014

2017

Current release

1703

8.0

3.1, March 2021

2022.1.6, December 2022

License

Commercial or Open Source

commercial

free trial version available

Open Source

AGPL 3.0, commercial license available

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

Java

C++

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux

AIX
Linux
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

optional

yes

yes, ANSI Standard SQL Types

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

yes

APIs and other access methods

LDAP

JDBC
ODBC
Proprietary protocol

JDBC
Native Spark Datasource
ODBC

.Net
JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

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

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

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Java

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

Atomic execution of specific operations

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

managed by 'Learn by Usage'

yes

User concepts

Access control

LDAP bind authentication

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

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