DBMS > Adabas vs. Blazegraph vs. Google Cloud Datastore vs. OpenQM vs. Sadas Engine

System Properties Comparison Adabas vs. Blazegraph vs. Google Cloud Datastore vs. OpenQM vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

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

used typically together with the Natural programming platform

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

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

QpenQM is a high-performance, self-tuning, multi-value DBMS

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

Primary database model

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

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

blazegraph.com

cloud.google.com/datastore

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

www.sadasengine.com

Technical documentation

wiki.blazegraph.com

cloud.google.com/datastore/docs

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

Developer

Software AG

Blazegraph

Google

Rocket Software, originally Martin Phillips

SADAS s.r.l.

Initial release

1971

2006

2008

1993

2006

Current release

2.1.5, March 2019

3.4-12

8.0

License

Commercial or Open Source

commercial

Open Source

extended commercial license available

commercial

Open Source

GPLv2, extended commercial license available

commercial

free trial version 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++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux
OS X
Windows

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

AIX
Linux
Windows

Data scheme

yes

schema-free

yes

with some exceptions

yes

Typing

predefined data types such as float or date

yes

RDF literal types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SPARQL is used as query language

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

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

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

JDBC
ODBC
Proprietary protocol

Supported programming languages

Natural

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

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

.Net
Basic
C
Java
Objective C
PHP
Python

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

Server-side scripts

Stored procedures

in Natural

yes

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

Multi-source replication using Paxos

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 configuration

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

Relationships in Graphs

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

managed by 'Learn by Usage'

User concepts

Access control

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

Security and Authentication via Web Application Container (Tomcat, Jetty)

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

Access rights can be defined down to the item level

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

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