DBMS > Amazon Neptune vs. Atos Standard Common Repository vs. Google BigQuery vs. Microsoft Access vs. RDF4J

System Properties Comparison Amazon Neptune vs. Atos Standard Common Repository vs. Google BigQuery vs. Microsoft Access vs. RDF4J

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Fast, reliable graph database built for the cloud

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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.)

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Graph DBMS
RDF store

Document store
Key-value store

Relational DBMS

RDF store

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

aws.amazon.com/neptune

atos.net/en/convergence-creators/portfolio/standard-common-repository

cloud.google.com/bigquery

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

rdf4j.org

Technical documentation

aws.amazon.com/neptune/developer-resources

cloud.google.com/bigquery/docs

developer.microsoft.com/en-us/access

rdf4j.org/documentation

Developer

Amazon

Atos Convergence Creators

Google

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2017

2016

2010

1992

2004

Current release

1703

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

Open Source

Eclipse Distribution License (EDL), v1.0.

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++

Java

Server operating systems

hosted

Linux

hosted

Windows

Not a real database server, but making use of DLLs

Linux
OS X
Unix
Windows

Data scheme

schema-free

Schema and schema-less with LDAP views

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

optional

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

LDAP

RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

yes

Triggers

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

cell division

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

Since BigQuery is designed for querying data

ACID

but no files for transaction logging

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

but no files for transaction logging

yes

in-memory storage is supported as well

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 can be defined via the AWS Identity and Access Management (IAM)

LDAP bind authentication

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

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