DBMS > Adabas vs. AnzoGraph DB vs. BigObject vs. Cassandra vs. HugeGraph

System Properties Comparison Adabas vs. AnzoGraph DB vs. BigObject vs. Cassandra vs. HugeGraph

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

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Analytic DBMS for real-time computations and queries

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

A fast-speed and highly-scalable Graph DBMS

Primary database model

Multivalue DBMS

Graph DBMS
RDF store

Relational DBMS

a hierachical model (tree) can be imposed

Wide column store

Graph DBMS

Secondary database models

Vector DBMS

starting with release V5

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

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

cambridgesemantics.com/anzograph

bigobject.io

cassandra.apache.org

github.com/hugegraph
hugegraph.apache.org

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

docs.bigobject.io

cassandra.apache.org/doc/latest

hugegraph.apache.org/docs

Developer

Software AG

Cambridge Semantics

BigObject, Inc.

Apache Software Foundation

Apache top level project, originally developped by Facebook

Baidu

Initial release

1971

2018

2015

2008

2018

Current release

2.3, January 2021

4.1.3, July 2023

0.9

License

Commercial or Open Source

commercial

free trial version available

commercial

free community edition available

Open Source

Apache version 2

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

BSD
Linux
OS X
Windows

Linux
macOS
Unix

Data scheme

yes

Schema-free and OWL/RDFS-schema support

yes

schema-free

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

restricted

only equality queries, not always the best performing solution

yes

also supports composite index and range index

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL-like DML and DDL statements

SQL-like SELECT, DML and DDL statements (CQL)

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

fluentd
ODBC
RESTful HTTP API

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

Natural

C++
Java
Python

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

Groovy
Java
Python

Server-side scripts

Stored procedures

in Natural

user defined functions and aggregates

Lua

asynchronous Gremlin script jobs

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

Automatic sharding

none

Sharding

no "single point of failure"

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

Multi-source replication in MPP-Cluster

none

selectable replication factor

Representation of geographical distribution of servers is possible

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

none

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

automatically between fact table and dimension tables

yes

edges in graph

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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 and roles

Access rights for users can be defined per object

Users, roles and permissions

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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