DBMS > Atos Standard Common Repository vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Postgres-XL vs. SQLite

System Properties Comparison Atos Standard Common Repository vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Postgres-XL vs. SQLite

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

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Widely used embeddable, in-process RDBMS

Primary database model

Document store
Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

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

www.kinetica.com

azure.microsoft.com/services/cosmos-db

www.postgres-xl.org

www.sqlite.org

Technical documentation

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

www.postgres-xl.org/documentation

www.sqlite.org/docs.html

Developer

Atos Convergence Creators

Kinetica

Microsoft

Dwayne Richard Hipp

Initial release

2016

2012

2014

since 2012, originally named StormDB

2000

Current release

1703

7.1, August 2021

10 R1, October 2018

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Open Source

Mozilla public license

Open Source

Public Domain

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

Java

C, C++

Server operating systems

Linux

hosted

Linux
macOS

server-less

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

optional

yes

JSON types

yes

not rigid because of 'dynamic typing' concept.

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

XML type, but no XML query functionality

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

distributed, parallel query execution

yes

SQL-92 is not fully supported

APIs and other access methods

LDAP

JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

All languages with LDAP bindings

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions

JavaScript

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Implicit feature of the cloud service

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

GPU vRAM or System RAM

yes

User concepts

Access control

LDAP bind authentication

Access rights for users and roles on table level

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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