DBMS > AnzoGraph DB vs. Fauna vs. HEAVY.AI vs. MonetDB vs. Snowflake

System Properties Comparison AnzoGraph DB vs. Fauna vs. HEAVY.AI vs. MonetDB 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

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A relational database management system that stores data in columns

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

Primary database model

Graph DBMS
RDF store

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.27
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	1.55
Rank	#154	Overall
	#26	Document stores
	#14	Graph DBMS
	#72	Relational DBMS
	#13	Time Series DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	1.72
Rank	#148	Overall
	#68	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

cambridgesemantics.com/anzograph

fauna.com

github.com/heavyai/heavydb
www.heavy.ai

www.monetdb.org

www.snowflake.com

Technical documentation

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

docs.fauna.com

docs.heavy.ai

www.monetdb.org/Documentation

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

Fauna, Inc.

HEAVY.AI, Inc.

MonetDB BV

Snowflake Computing Inc.

Initial release

2018

2014

2016

2004

2014

Current release

2.3, January 2021

5.10, January 2022

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Mozilla Public License 2.0

commercial

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

Scala

C++ and CUDA

Server operating systems

Linux

hosted

Linux

FreeBSD
Linux
OS X
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

SQL 2003 with some extensions

yes

APIs and other access methods

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

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Python

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

yes, in SQL, C, R

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

horizontal partitioning

consistent hashing

Sharding

Round robin

Sharding via remote tables

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication

none

Source-replica replication available in experimental status

yes

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

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

Identity management, authentication, and access control

fine grained access rights according to SQL-standard

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

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