DBMS > AnzoGraph DB vs. Citus vs. Ehcache vs. Google BigQuery vs. Netezza

System Properties Comparison AnzoGraph DB vs. Citus vs. Ehcache vs. Google BigQuery vs. Netezza

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

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

A widely adopted Java cache with tiered storage options

Large scale data warehouse service with append-only tables

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

Secondary database models

Document store

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	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.citusdata.com

www.ehcache.org

cloud.google.com/bigquery

www.ibm.com/products/netezza

Technical documentation

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

docs.citusdata.com

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

Developer

Cambridge Semantics

Terracotta Inc, owned by Software AG

Google

IBM

Initial release

2018

2010

2009

2010

2000

Current release

2.3, January 2021

8.1, December 2018

3.10.0, March 2022

License

Commercial or Open Source

commercial

free trial version available

Open Source

AGPL, commercial license also available

Open Source

Apache Version 2; commercial licenses available

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

Java

Server operating systems

Linux

All OS with a Java VM

hosted

Linux

included in appliance

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

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

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

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

yes

standard, with numerous extensions

yes

APIs and other access methods

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

JCache

RESTful HTTP/JSON API

JDBC
ODBC
OLE DB

Supported programming languages

C++
Java
Python

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

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

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

in JavaScript

yes

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

by using Terracotta Server

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

other methods possible by using 3rd party extensions

yes

by using Terracotta Server

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

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

supports JTA and can work as an XA resource

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.