DBMS > AnzoGraph DB vs. Firebase Realtime Database vs. JanusGraph vs. MaxDB vs. Snowflake

System Properties Comparison AnzoGraph DB vs. Firebase Realtime Database vs. JanusGraph vs. MaxDB vs. Snowflake

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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

A robust and reliable RDBMS optimized to run all major SAP solutions

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

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

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

cambridgesemantics.com/anzograph

firebase.google.com/products/realtime-database

janusgraph.org

maxdb.sap.com

www.snowflake.com

Technical documentation

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

firebase.google.com/docs/database

docs.janusgraph.org

maxdb.sap.com/documentation

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

Google

acquired by Google 2014

Linux Foundation; originally developed as Titan by Aurelius

SAP, acquired from Software AG (Adabas-D) in 1997

Snowflake Computing Inc.

Initial release

2018

2012

2017

1984

2014

Current release

2.3, January 2021

0.6.3, February 2023

7.9.10.12, February 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

Apache 2.0

commercial

Limited community edition free

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux

hosted

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

APIs and other access methods

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

Android
iOS
JavaScript API
RESTful HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC
WebDAV

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Python

Java
JavaScript
Objective-C

Clojure
Java
Python

.Net
C#
Java
Perl
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

limited functionality with using 'rules'

yes

user defined functions

Triggers

Callbacks are triggered when data changes

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

yes, based on authentication and database rules

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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