DBMS > Adabas vs. Apache IoTDB vs. Microsoft SQL Server vs. OpenEdge vs. SiteWhere

System Properties Comparison Adabas vs. Apache IoTDB vs. Microsoft SQL Server vs. OpenEdge vs. SiteWhere

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

Microsofts flagship relational DBMS

Application development environment with integrated database management system

M2M integration platform for persisting/querying time series data

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

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

iotdb.apache.org

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

www.progress.com/openedge

github.com/sitewhere/sitewhere

Technical documentation

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

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

documentation.progress.com/output/ua/OpenEdge_latest

sitewhere1.sitewhere.io/index.html

Developer

Software AG

Apache Software Foundation

Microsoft

Progress Software Corporation

SiteWhere

Initial release

1971

2018

1989

1984

2010

Current release

1.1.0, April 2023

SQL Server 2022, November 2022

OpenEdge 12.2, March 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Java

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

All OS with a Java VM (>= 1.8)

Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

predefined scheme

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

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL-like query language

yes

close to SQL 92

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

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

JDBC
ODBC

HTTP REST

Supported programming languages

Natural

C
C#
C++
Go
Java
Python
Scala

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

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

in Natural

yes

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

yes

Triggers

yes

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)

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

horizontal partitioning

since Version 11.4

Sharding

based on HBase

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

yes, but depending on the SQL-Server Edition

Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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)

yes

fine grained access rights according to SQL-standard

Users and groups

Users with fine-grained authorization concept

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