DBMS > AllegroGraph vs. Bangdb vs. Fujitsu Enterprise Postgres vs. Google BigQuery

System Properties Comparison AllegroGraph vs. Bangdb vs. Fujitsu Enterprise Postgres vs. Google BigQuery

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Editorial information provided by DB-Engines

Name

Description

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

Converged and high performance database for device data, events, time series, document and graph

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

Large scale data warehouse service with append-only tables

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
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	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.31
Rank	#285	Overall
	#129	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Website

allegrograph.com

bangdb.com

www.postgresql.fastware.com

cloud.google.com/bigquery

Technical documentation

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

docs.bangdb.com

www.postgresql.fastware.com/product-manuals

cloud.google.com/bigquery/docs

Developer

Franz Inc.

Sachin Sinha, BangDB

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Google

Initial release

2004

2012

2010

Current release

8.0, December 2023

BangDB 2.0, October 2021

Fujitsu Enterprise Postgres 14, January 2022

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

BSD 3

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, C++

Server operating systems

Linux
OS X
Windows

Linux

Linux
Windows

hosted

Data scheme

yes

RDF schemas

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SPARQL is used as query language

SQL like support with command line tool

yes

APIs and other access methods

RESTful HTTP API
SPARQL

Proprietary protocol
RESTful HTTP API

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

RESTful HTTP/JSON API

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

C
C#
C++
Java
Python

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

user defined functions

in JavaScript

Triggers

yes

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

with Federation

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

partitioning by range, list and by hash

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication

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

Tunable consistency, set CAP knob accordingly

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

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

User concepts

Access control

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

yes (enterprise version only)

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)

More information provided by the system vendor

Specific characteristics

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

100% compatible with community PostgreSQL
» more

Competitive advantages

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

Built-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more

Typical application scenarios

Transactional payments applications, reporting and mixed workloads.
» more

Market metrics

Over 30 years experience in database technology. Over 20 years in Postgres development...
» more

Licensing and pricing models

Core based licensing
» 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

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