DBMS > AgensGraph vs. BaseX vs. Google BigQuery vs. Microsoft Access vs. TigerGraph

System Properties Comparison AgensGraph vs. BaseX vs. Google BigQuery vs. Microsoft Access vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Large scale data warehouse service with append-only tables

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Graph DBMS
Relational DBMS

Native XML DBMS

Relational DBMS

Graph DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Website

bitnine.net/agensgraph

basex.org

cloud.google.com/bigquery

www.microsoft.com/en-us/microsoft-365/access

www.tigergraph.com

Technical documentation

bitnine.net/documentation

docs.basex.org

cloud.google.com/bigquery/docs

developer.microsoft.com/en-us/access

docs.tigergraph.com

Developer

Bitnine Global Inc.

BaseX GmbH

Google

Microsoft

Initial release

2016

2007

2010

1992

2017

Current release

2.1, December 2018

11.0, June 2024

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

BSD license

commercial

Bundled with Microsoft Office

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++

Server operating systems

Linux
OS X
Windows

hosted

Windows

Not a real database server, but making use of DLLs

Linux

Data scheme

depending on used data model

schema-free

yes

Typing

predefined data types such as float or date

yes

XQuery supports types

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

yes

but not compliant to any SQL standard

SQL-like query language (GSQL)

APIs and other access methods

Cypher Query Language
JDBC

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

C
Java
JavaScript
Python

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

C++
Java

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

yes

Triggers

yes

via events

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

multiple readers, single writer

Since BigQuery is designed for querying data

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept on 4 levels

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

Google Cloud Identity & Access Management (IAM)

a simple user-level security was built in till version Access 2003

Role-based access control

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