DBMS > Adabas vs. Atos Standard Common Repository vs. GridGain vs. Hyprcubd vs. Microsoft SQL Server

System Properties Comparison Adabas vs. Atos Standard Common Repository vs. GridGain vs. Hyprcubd vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

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

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

used typically together with the Natural programming platform

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

GridGain is an in-memory computing platform, built on Apache Ignite

Serverless Time Series DBMS

Microsofts flagship relational DBMS

Primary database model

Multivalue DBMS

Document store
Key-value store

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	2.64
Rank	#104	Overall
	#1	Multivalue DBMS

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Website

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

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

www.gridgain.com

hyprcubd.com (offline)

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

Technical documentation

www.gridgain.com/docs/index.html

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

Developer

Software AG

Atos Convergence Creators

GridGain Systems, Inc.

Hyprcubd, Inc.

Microsoft

Initial release

1971

2016

2007

1989

Current release

1703

GridGain 8.5.1

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

commercial, open source

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

Java, C++, .Net, Python, REST, SQL

C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux

Linux
OS X
Solaris
Windows
z/OS

hosted

Linux
Windows

Data scheme

yes

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

yes

optional

yes

time, int, uint, float, string

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

yes

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

LDAP

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

gRPC (https)

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

Supported programming languages

Natural

All languages with LDAP bindings

C#
C++
Java
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

in Natural

yes (compute grid and cache interceptors can be used instead)

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

Triggers

yes

yes (cache interceptors and events)

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

cell division

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

yes (replicated cache)

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

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

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

LDAP bind authentication

Role-based access control
Security Hooks for custom implementations

token access

fine grained access rights according to SQL-standard

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