DBMS > AllegroGraph vs. Google BigQuery vs. HyperSQL vs. JaguarDB vs. Microsoft Azure Table Storage

System Properties Comparison AllegroGraph vs. Google BigQuery vs. HyperSQL vs. JaguarDB vs. Microsoft Azure Table Storage

Editorial information provided by DB-Engines

Name

Description

High performance, persistent RDF store with additional support for Graph DBMS

Large scale data warehouse service with append-only tables

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Performant, highly scalable DBMS for AI and IoT applications

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Relational DBMS

Key-value store
Vector DBMS

Wide column store

Secondary database models

Spatial DBMS

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

Score	1.06
Rank	#187	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Website

allegrograph.com

cloud.google.com/bigquery

hsqldb.org

www.jaguardb.com

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

cloud.google.com/bigquery/docs

hsqldb.org/web/hsqlDocsFrame.html

www.jaguardb.com/support.html

Developer

Franz Inc.

Google

DataJaguar, Inc.

Microsoft

Initial release

2004

2010

2001

2015

2012

Current release

8.0, December 2023

2.7.2, June 2023

3.3 July 2023

License

Commercial or Open Source

commercial

Limited community edition free

commercial

Open Source

based on BSD license

Open Source

GPL V3.0

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

Java

C++

the server part. Clients available in other languages

Server operating systems

Linux
OS X
Windows

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux

hosted

Data scheme

yes

RDF schemas

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.

bulk load of XML files possible

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

RESTful HTTP API
SPARQL

RESTful HTTP/JSON API

HTTP API

JDBC via HTTP
JDBC
ODBC

JDBC
ODBC

RESTful HTTP API

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC
Java

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

user defined functions

in JavaScript

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

with Federation

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Multi-source replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

optimistic locking

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

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

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

rights management via user accounts

Access rights based on private key authentication or shared access signatures

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more

Competitive advantages

AllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more

News

Can Neuro-Symbolic AI Solve AI’s Weaknesses?
17 April 2024

100 Companies That Matter in KM – Franz Inc.
3 April 2024

Exploring AllegroGraph v8 – Unleashing the Power of Neuro-Symbolic AI (Recorded Webinar)
9 February 2024

What is Neuro-Symbolic AI?
23 January 2024

Allegro CL v11 – Now Available! – The Neuro-Symbolic AI Programming Platform
8 January 2024

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.