DBMS > AnzoGraph DB vs. DataFS vs. dBASE vs. Google BigQuery vs. Splice Machine

System Properties Comparison AnzoGraph DB vs. DataFS vs. dBASE vs. Google BigQuery vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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.

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

Object oriented DBMS

Relational DBMS

Secondary database models

Graph DBMS

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Trend Chart

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	9.16
Rank	#43	Overall
	#27	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

cambridgesemantics.com/anzograph

newdatabase.com

www.dbase.com

cloud.google.com/bigquery

splicemachine.com

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

dev.mobiland.com/Overview.xsp

www.dbase.com/support/knowledgebase

cloud.google.com/bigquery/docs

splicemachine.com/how-it-works

Developer

Cambridge Semantics

Mobiland AG

Asthon Tate

Google

Splice Machine

Initial release

2018

1979

2010

2014

Current release

2.3, January 2021

1.1.263, October 2022

dBASE 2019, 2019

3.1, March 2021

License

Commercial or Open Source

commercial

free trial version 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

Windows

DOS

dBase Classic
Windows

dBase Pro

hosted

Linux
OS X
Solaris
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Typing

predefined data types such as float or date

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 and SPARQL* as primary query language. Cypher preview.

yes

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

.NET Client API
Proprietary client DLL
WinRT client

none

The IDE can access other DBMS or ODBC-sources.

RESTful HTTP/JSON API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C++
Java
Python

.Net
C
C#
C++
VB.Net

dBase proprietary IDE

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions and aggregates

The IDE can access stored procedures in other database systems.

user defined functions

in JavaScript

yes

Java

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Proprietary Sharding system

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

not needed 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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

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

Access rights for users and roles

Windows-Profile

Access rights for users and roles

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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