DBMS > DataFS vs. dBASE vs. Google BigQuery vs. Splice Machine vs. TimescaleDB

System Properties Comparison DataFS vs. dBASE vs. Google BigQuery vs. Splice Machine vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Object oriented DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Graph DBMS

Relational DBMS

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

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	9.70
Rank	#44	Overall
	#28	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

newdatabase.com

www.dbase.com

cloud.google.com/bigquery

splicemachine.com

www.timescale.com

Technical documentation

dev.mobiland.com/Overview.xsp

www.dbase.com/support/knowledgebase

cloud.google.com/bigquery/docs

splicemachine.com/how-it-works

docs.timescale.com

Developer

Mobiland AG

Asthon Tate

Google

Splice Machine

Timescale

Initial release

2018

1979

2010

2014

2017

Current release

1.1.263, October 2022

dBASE 2019, 2019

3.1, March 2021

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Apache 2.0

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

Server operating systems

Windows

DOS

dBase Classic
Windows

dBase Pro

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

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

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

full PostgreSQL SQL syntax

APIs and other access methods

.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

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

Supported programming languages

.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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

The IDE can access stored procedures in other database systems.

user defined functions

in JavaScript

yes

Java

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Source-replica replication with hot standby and reads on replicas

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

Foreign keys

Referential integrity

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)

yes

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

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

fine grained access rights according to SQL-standard

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