DBMS > Adabas vs. Apache IoTDB vs. Google BigQuery vs. Oracle

System Properties Comparison Adabas vs. Apache IoTDB vs. Google BigQuery vs. Oracle

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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

Large scale data warehouse service with append-only tables

Widely used RDBMS

Primary database model

Multivalue DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Website

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

iotdb.apache.org

cloud.google.com/bigquery

www.oracle.com/database

Technical documentation

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

cloud.google.com/bigquery/docs

docs.oracle.com/en/database

Developer

Software AG

Apache Software Foundation

Google

Oracle

Initial release

1971

2018

2010

1980

Current release

1.1.0, April 2023

23c, September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

restricted free version is available

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 and C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

All OS with a Java VM (>= 1.8)

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

yes

Schemaless in JSON and XML columns

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

with proprietary extensions

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

RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Natural

C
C#
C++
Go
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Server-side scripts

Stored procedures

in Natural

yes

user defined functions

in JavaScript

PL/SQL

also stored procedures in Java possible

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)

none

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

can be realized in PL/SQL

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

Since BigQuery is designed for querying data

ACID

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

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

yes

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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