DBMS > Adabas vs. Apache IoTDB vs. Cassandra vs. GridGain vs. Sadas Engine

System Properties Comparison Adabas vs. Apache IoTDB vs. Cassandra vs. GridGain vs. Sadas Engine

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

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Multivalue DBMS

Time Series DBMS

Wide column store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

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

Score	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	98.83
Rank	#12	Overall
	#1	Wide column stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

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

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

cassandra.apache.org/doc/latest

www.gridgain.com/docs/index.html

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Software AG

Apache Software Foundation

Apache top level project, originally developped by Facebook

GridGain Systems, Inc.

SADAS s.r.l.

Initial release

1971

2018

2008

2007

2006

Current release

1.1.0, April 2023

4.1.3, July 2023

GridGain 8.5.1

8.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache version 2

commercial

free trial version available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Java, C++, .Net

C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

All OS with a Java VM (>= 1.8)

BSD
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

AIX
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL-like query language

SQL-like SELECT, DML and DDL statements (CQL)

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

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

JDBC
Native API

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

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

JDBC
ODBC
Proprietary protocol

Supported programming languages

Natural

C
C#
C++
Go
Java
Python
Scala

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

in Natural

yes

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

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

no "single point of failure"

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

selectable replication factor

Representation of geographical distribution of servers is possible

yes (replicated cache)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomicity and isolation are supported for single 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

managed by 'Learn by Usage'

User concepts

Access control

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

yes

Access rights for users can be defined per object

Security Hooks for custom implementations

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

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