DBMS > Blazegraph vs. dBASE vs. Fujitsu Enterprise Postgres vs. Google BigQuery vs. Splice Machine

System Properties Comparison Blazegraph vs. dBASE vs. Fujitsu Enterprise Postgres vs. Google BigQuery vs. Splice Machine

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.

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Document store
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	9.16
Rank	#43	Overall
	#27	Relational DBMS

Score	0.22
Rank	#304	Overall
	#137	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

blazegraph.com

www.dbase.com

www.postgresql.fastware.com

cloud.google.com/bigquery

splicemachine.com

Technical documentation

wiki.blazegraph.com

www.dbase.com/support/knowledgebase

www.postgresql.fastware.com/product-manuals

cloud.google.com/bigquery/docs

splicemachine.com/how-it-works

Developer

Blazegraph

Asthon Tate

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Google

Splice Machine

Initial release

2006

1979

2010

2014

Current release

2.1.5, March 2019

dBASE 2019, 2019

Fujitsu Enterprise Postgres 14, January 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

extended commercial license available

commercial

Open Source

AGPL 3.0, commercial license available

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

Server operating systems

Linux
OS X
Windows

DOS

dBase Classic
Windows

dBase Pro

Linux
Windows

hosted

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal 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

SPARQL is used as query language

yes

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

none

The IDE can access other DBMS or ODBC-sources.

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

RESTful HTTP/JSON API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

dBase proprietary IDE

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

The IDE can access stored procedures in other database systems.

user defined functions

in JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

partitioning by range, list and by hash

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

not for dBase internal data, but IDE does support transactions when accessing external DBMS

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

yes, multi-version concurrency control (MVCC)

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

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

Access rights for users and roles

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)

Access rights for users, groups and roles according to SQL-standard

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