DBMS > Blazegraph vs. Google Cloud Datastore vs. Microsoft Access vs. Microsoft SQL Server

System Properties Comparison Blazegraph vs. Google Cloud Datastore vs. Microsoft Access vs. Microsoft SQL Server

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Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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.)

Microsofts flagship relational DBMS

Primary database model

Graph DBMS
RDF store

Document store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.74
Rank	#217	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	93.76
Rank	#12	Overall
	#8	Relational DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Website

blazegraph.com

cloud.google.com/datastore

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

www.microsoft.com/en-us/sql-server

Technical documentation

wiki.blazegraph.com

cloud.google.com/datastore/docs

developer.microsoft.com/en-us/access

learn.microsoft.com/en-US/sql/sql-server

Developer

Blazegraph

Google

Microsoft

Initial release

2006

2008

1992

1989

Current release

2.1.5, March 2019

1902 (16.0.11328.20222), March 2019

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

extended commercial license available

commercial

Bundled with Microsoft Office

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Windows

hosted

Windows

Not a real database server, but making use of DLLs

Linux
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

yes, details here

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

SPARQL is used as query language

SQL-like query language (GQL)

yes

but not compliant to any SQL standard

yes

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

since Access 2010 using the ACE-engine

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

Callbacks using the Google Apps Engine

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

none

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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.

yes

User concepts

Access control

Security and Authentication via Web Application Container (Tomcat, Jetty)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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

fine grained access rights according to SQL-standard

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