DBMS > AnzoGraph DB vs. Google BigQuery vs. Netezza vs. RethinkDB vs. Sadas Engine

System Properties Comparison AnzoGraph DB vs. Google BigQuery vs. Netezza vs. RethinkDB vs. Sadas Engine

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

Large scale data warehouse service with append-only tables

Data warehouse and analytics appliance part of IBM PureSystems

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Secondary database models

Spatial DBMS

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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

cambridgesemantics.com/anzograph

cloud.google.com/bigquery

www.ibm.com/products/netezza

rethinkdb.com

www.sadasengine.com

Technical documentation

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

cloud.google.com/bigquery/docs

rethinkdb.com/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Cambridge Semantics

Google

IBM

The Linux Foundation

since July 2017

SADAS s.r.l.

Initial release

2018

2010

2000

2009

2006

Current release

2.3, January 2021

2.4.1, August 2020

8.0

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

Apache Version 2

commercial

free trial version available

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++

Server operating systems

Linux

hosted

Linux

included in appliance

Linux
OS X
Windows

AIX
Linux
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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.

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

RESTful HTTP/JSON API

JDBC
ODBC
OLE DB

JDBC
ODBC
Proprietary protocol

Supported programming languages

C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Fortran
Java
Lua
Perl
Python
R

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

yes

Triggers

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

range based

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

none

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

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

managed by 'Learn by Usage'

User concepts

Access control

Access rights for users and roles

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

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept

yes

users and table-level permissions

Access rights for users, groups and roles according to SQL-standard

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