DBMS > AnzoGraph DB vs. Firebase Realtime Database vs. H2 vs. Microsoft Azure Table Storage vs. XTDB

System Properties Comparison AnzoGraph DB vs. Firebase Realtime Database vs. H2 vs. Microsoft Azure Table Storage vs. XTDB

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.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

A Wide Column Store for rapid development using massive semi-structured datasets

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	7.57
Rank	#47	Overall
	#30	Relational DBMS

Score	3.55
Rank	#80	Overall
	#6	Wide column stores

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

cambridgesemantics.com/anzograph

firebase.google.com/products/realtime-database

www.h2database.com

azure.microsoft.com/en-us/services/storage/tables

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

firebase.google.com/docs/database

www.h2database.com/html/main.html

www.xtdb.com/docs

Developer

Cambridge Semantics

Google

acquired by Google 2014

Thomas Mueller

Microsoft

Juxt Ltd.

Initial release

2018

2012

2005

2012

2019

Current release

2.3, January 2021

2.2.220, July 2023

1.19, September 2021

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

Open Source

MIT License

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

Clojure

Server operating systems

Linux

hosted

All OS with a Java VM

hosted

All OS with a Java 8 (and higher) VM
Linux

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

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

yes

limited SQL, making use of Apache Calcite

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

JDBC
ODBC

RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

C++
Java
Python

Java
JavaScript
Objective-C

Java

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

user defined functions and aggregates

limited functionality with using 'rules'

Java Stored Procedures and User-Defined Functions

Triggers

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

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

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

yes

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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