DBMS > Adabas vs. Google Cloud Datastore vs. Microsoft Azure AI Search vs. SAP HANA vs. Yanza

System Properties Comparison Adabas vs. Google Cloud Datastore vs. Microsoft Azure AI Search vs. SAP HANA vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Search-as-a-service for web and mobile app development

In-memory, column based data store. Available as appliance or cloud service

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Vector DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

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

cloud.google.com/datastore

azure.microsoft.com/en-us/services/search

www.sap.com/products/hana.html

yanza.com

Technical documentation

cloud.google.com/datastore/docs

learn.microsoft.com/en-us/azure/search

help.sap.com/hana

Developer

Software AG

Google

Microsoft

SAP

Yanza

Initial release

1971

2008

2015

2010

2015

Current release

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

Appliance or cloud-service

Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL-like query language (GQL)

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

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

RESTful HTTP API

JDBC
ODBC

HTTP API

Supported programming languages

Natural

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java
JavaScript
Python

any language that supports HTTP calls

Server-side scripts

Stored procedures

in Natural

using Google App Engine

SQLScript, R

Triggers

Callbacks using the Google Apps Engine

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Implicit feature of the cloud service

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

Multi-source replication using Paxos

yes

Implicit feature of the cloud service

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

using Azure authentication

yes

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