DBMS > Adabas vs. Atos Standard Common Repository vs. Google BigQuery vs. Solr vs. SWC-DB

System Properties Comparison Adabas vs. Atos Standard Common Repository vs. Google BigQuery vs. Solr vs. SWC-DB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed 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

Large scale data warehouse service with append-only tables

A widely used distributed, scalable search engine based on Apache Lucene

A high performance, scalable Wide Column DBMS

Primary database model

Multivalue DBMS

Document store
Key-value store

Relational DBMS

Search engine

Wide column store

Secondary database models

Spatial DBMS

Time Series DBMS

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

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

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

cloud.google.com/bigquery

solr.apache.org

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

Technical documentation

cloud.google.com/bigquery/docs

solr.apache.org/resources.html

Developer

Software AG

Atos Convergence Creators

Google

Apache Software Foundation

Alex Kashirin

Initial release

1971

2016

2010

2006

2020

Current release

1703

9.6.0, April 2024

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GPL V3

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++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux

Data scheme

yes

Schema and schema-less with LDAP views

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes

optional

yes

supports customizable data types and automatic typing

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 search fields are automatically indexed

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

Solr Parallel SQL Interface

SQL-like query language

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

RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

Proprietary protocol
Thrift

Supported programming languages

Natural

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++

Server-side scripts

Stored procedures

in Natural

user defined functions

in JavaScript

Java plugins

Triggers

yes

User configurable commands triggered on index changes

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

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

Transaction concepts

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

ACID

Atomic execution of specific operations

Since BigQuery is designed for querying data

optimistic locking

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

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