DBMS > AnzoGraph DB vs. BigObject vs. Google BigQuery vs. Ingres vs. SiriDB

System Properties Comparison AnzoGraph DB vs. BigObject vs. Google BigQuery vs. Ingres vs. SiriDB

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

Analytic DBMS for real-time computations and queries

Large scale data warehouse service with append-only tables

Well established RDBMS

Open Source Time Series DBMS

Primary database model

Graph DBMS
RDF store

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Time Series DBMS

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

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Website

cambridgesemantics.com/anzograph

bigobject.io

cloud.google.com/bigquery

www.actian.com/databases/ingres

siridb.com

Technical documentation

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

docs.bigobject.io

cloud.google.com/bigquery/docs

docs.actian.com/ingres

docs.siridb.com

Developer

Cambridge Semantics

BigObject, Inc.

Google

Actian Corporation

Cesbit

Initial release

2018

2015

2010

1974

originally developed at University Berkely in early 1970s

2017

Current release

2.3, January 2021

11.2, May 2022

License

Commercial or Open Source

commercial

free trial version available

commercial

free community edition available

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

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

yes

Numeric data

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like DML and DDL statements

yes

APIs and other access methods

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

fluentd
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

HTTP API

Supported programming languages

C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions and aggregates

Lua

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

none

Ingres Replicator

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

none

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

MVCC

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

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

simple rights management via user accounts

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