DBMS > Adabas vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Teradata vs. TimescaleDB

System Properties Comparison Adabas vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Teradata vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

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

Multivalue DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial 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	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

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	44.87
Rank	#22	Overall
	#15	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

www.softwareag.com/en_corporate/platform/adabas-natural.html

www.kinetica.com

azure.microsoft.com/services/cosmos-db

www.teradata.com

www.timescale.com

Technical documentation

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

docs.teradata.com

docs.timescale.com

Developer

Software AG

Kinetica

Microsoft

Teradata

Timescale

Initial release

1971

2012

2014

1984

2017

Current release

7.1, August 2021

Teradata Vantage 1.0 MU2, January 2019

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

C, C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux

hosted

hosted
Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

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

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL-like DML and DDL statements

SQL-like query language

yes

SQL 2016 + extensions

yes

full PostgreSQL SQL syntax

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

JDBC
ODBC
RESTful HTTP API

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

.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

Natural

C++
Java
JavaScript (Node.js)
Python

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

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

in Natural

user defined functions

JavaScript

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

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

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

Sharding

Implicit feature of the cloud service

Sharding

Hashing

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

Source-replica replication

yes

Implicit feature of the cloud service

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

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

GPU vRAM or System RAM

yes

User concepts

Access control

only with OS-specific tools (e.g. IBM RACF, CA Top Secret)

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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