DBMS > AnzoGraph DB vs. GridGain vs. SAP HANA

System Properties Comparison AnzoGraph DB vs. GridGain vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

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

GridGain is an in-memory computing platform, built on Apache Ignite

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

Primary database model

Graph DBMS
RDF store

Columnar
Key-value store
Object Oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.22
Rank	#305	Overall
	#25	Graph DBMS
	#13	RDF stores

Score	1.44
Rank	#152	Overall
	#1	Columnar
	#26	Key-value stores
	#1	Object Oriented DBMS
	#70	Relational DBMS

Score	44.12
Rank	#23	Overall
	#16	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.gridgain.com

www.sap.com/products/hana.html

Technical documentation

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

www.gridgain.com/docs/index.html

help.sap.com/hana

Developer

Cambridge Semantics

GridGain Systems, Inc.

SAP

Initial release

2018

2007

2010

Current release

2.3, January 2021

GridGain 8.5.1

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

free trial version available

commercial, open source

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java, C++, .Net, Python, REST, SQL

Server operating systems

Linux

Linux
OS X
Solaris
Windows
z/OS

Appliance or cloud-service

Data scheme

Schema-free and OWL/RDFS-schema support

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.

yes

Secondary indexes

yes

SQL

Support of SQL

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

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

Supported programming languages

C++
Java
Python

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

yes (compute grid and cache interceptors can be used instead)

SQLScript, R

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes (replicated cache)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

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

Access rights for users and roles

Role-based access control
Security Hooks for custom implementations

yes

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