DBMS > Adabas vs. Google BigQuery vs. Microsoft Azure Table Storage vs. OrientDB vs. TigerGraph

System Properties Comparison Adabas vs. Google BigQuery vs. Microsoft Azure Table Storage vs. OrientDB vs. TigerGraph

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

Large scale data warehouse service with append-only tables

A Wide Column Store for rapid development using massive semi-structured datasets

Multi-model DBMS (Document, Graph, Key/Value)

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Multivalue DBMS

Relational DBMS

Wide column store

Document store
Graph DBMS
Key-value store

Graph 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	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

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

cloud.google.com/bigquery

azure.microsoft.com/en-us/services/storage/tables

orientdb.org

www.tigergraph.com

Technical documentation

cloud.google.com/bigquery/docs

www.orientdb.com/docs/last/index.html

docs.tigergraph.com

Developer

Software AG

Google

Microsoft

OrientDB LTD; CallidusCloud; SAP

Initial release

1971

2010

2012

2010

2017

Current release

3.2.29, March 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

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

hosted

All OS with a Java JDK (>= JDK 6)

Linux

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

SQL-like query language, no joins

SQL-like query language (GSQL)

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

RESTful HTTP/JSON API

RESTful HTTP API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

Natural

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C++
Java

Server-side scripts

Stored procedures

in Natural

user defined functions

in JavaScript

Java, Javascript

yes

Triggers

Hooks

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Relationships in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

optimistic locking

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.

User concepts

Access control

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

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

Google Cloud Identity & Access Management (IAM)

Access rights based on private key authentication or shared access signatures

Access rights for users and roles; record level security configurable

Role-based access control

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