DBMS > Atos Standard Common Repository vs. Microsoft SQL Server vs. SWC-DB vs. Teradata vs. TimescaleDB

System Properties Comparison Atos Standard Common Repository vs. Microsoft SQL Server vs. SWC-DB vs. Teradata vs. TimescaleDB

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

Microsofts flagship relational DBMS

A high performance, scalable Wide Column DBMS

A hybrid cloud data analytics software platform (Teradata Vantage)

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store
Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

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

www.microsoft.com/en-us/sql-server

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

www.teradata.com

www.timescale.com

Technical documentation

learn.microsoft.com/en-US/sql/sql-server

docs.teradata.com

docs.timescale.com

Developer

Atos Convergence Creators

Microsoft

Alex Kashirin

Teradata

Timescale

Initial release

2016

1989

2020

1984

2017

Current release

1703

SQL Server 2022, November 2022

0.5, April 2021

Teradata Vantage 1.0 MU2, January 2019

2.15.0, May 2024

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GPL V3

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

Linux

Linux
Windows

Linux

hosted
Linux

Linux
OS X
Windows

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

yes

Typing

predefined data types such as float or date

optional

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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

Join-index to prejoin tables, aggregate index, sparse index, hash index

yes

SQL

Support of SQL

yes

SQL-like query language

yes

SQL 2016 + extensions

yes

full PostgreSQL SQL syntax

APIs and other access methods

LDAP

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Proprietary protocol
Thrift

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

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

Supported programming languages

All languages with LDAP bindings

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C++

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

UDFs, stored procedures, table functions in parallel

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Hashing

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

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

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

LDAP bind authentication

fine grained access rights according to SQL-standard

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