DBMS > Amazon Redshift vs. AnzoGraph DB vs. Citus vs. Drizzle vs. Fauna

System Properties Comparison Amazon Redshift vs. AnzoGraph DB vs. Citus vs. Drizzle vs. Fauna

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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.

Primary database model

Relational DBMS

Graph DBMS
RDF store

Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Secondary database models

Document store

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	1.55
Rank	#151	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Website

aws.amazon.com/redshift

cambridgesemantics.com/anzograph

www.citusdata.com

fauna.com

Technical documentation

docs.aws.amazon.com/redshift

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

docs.citusdata.com

docs.fauna.com

Developer

Amazon (based on PostgreSQL)

Cambridge Semantics

Drizzle project, originally started by Brian Aker

Fauna, Inc.

Initial release

2012

2018

2010

2008

2014

Current release

2.3, January 2021

8.1, December 2018

7.2.4, September 2012

License

Commercial or Open Source

commercial

free trial version available

Open Source

AGPL, commercial license also available

Open Source

GNU GPL

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

C++

Scala

Server operating systems

hosted

Linux

FreeBSD
Linux
OS X

hosted

Data scheme

yes

Schema-free and OWL/RDFS-schema support

yes

schema-free

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

specific XML type available, but no XML query functionality

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

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

yes

standard, with numerous extensions

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Python

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C++
Java
PHP

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions

Triggers

yes

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

horizontal partitioning

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication
Source-replica replication

Multi-source replication

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

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

fine grained access rights according to SQL-standard

Access rights for users and roles

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Identity management, authentication, and access control

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