DBMS > DataFS vs. Kinetica vs. ObjectBox vs. Valentina Server

System Properties Comparison DataFS vs. Kinetica vs. ObjectBox vs. Valentina Server

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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.

Fully vectorized database across both GPUs and CPUs

Lightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors

Object-relational database and reports server

Primary database model

Object oriented DBMS

Relational DBMS

Object oriented DBMS
Vector DBMS

Relational DBMS

Secondary database models

Graph DBMS

Spatial DBMS
Time Series DBMS

Time Series DBMS

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

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	1.08
Rank	#179	Overall
	#6	Object oriented DBMS
	#9	Vector DBMS

Score	0.18
Rank	#313	Overall
	#140	Relational DBMS

Website

newdatabase.com

www.kinetica.com

github.com/objectbox
objectbox.io

www.valentina-db.net

Technical documentation

dev.mobiland.com/Overview.xsp

docs.kinetica.com

docs.objectbox.io

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Mobiland AG

Kinetica

ObjectBox Limited

Paradigma Software

Initial release

2018

2012

2017

1999

Current release

1.1.263, October 2022

7.1, August 2021

4.0 (May 2024)

5.7.5

License

Commercial or Open Source

commercial

Bindings are released under Apache 2.0

Apache License 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

C and C++

Server operating systems

Windows

Linux

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
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

yes, plus "flex" map-like 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

SQL-like DML and DDL statements

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC
RESTful HTTP API

Proprietary native API

ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

C++
Java
JavaScript (Node.js)
Python

C
C++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

no, except callback-events from server when changes happened

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Data sync between devices allowing occasional connected databases to work completely offline

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

GPU vRAM or System RAM

yes

User concepts

Access control

Windows-Profile

Access rights for users and roles on table level

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

MongoDB Realm & Device Sync alternatives – ObjectBox
17 September 2024

Local AI – what it is and why we need it
11 September 2024

First on-device Vector Database (aka Semantic Index) for iOS
24 July 2024

Retrieval Augmented Generation (RAG) with vector databases: Expanding AI Capabilities
18 June 2024

The on-device Vector Database for Android and Java
29 May 2024

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More resources

Recent citations in the news

Kinetica Elevates RAG with Fast Access to Real-Time Data
26 March 2024, Datanami

Kinetica Delivers Real-Time Vector Similarity Search
21 March 2024, insideBIGDATA

Kinetica ramps up RAG for generative AI, empowering enterprises with real-time operational data
18 March 2024, SiliconANGLE News

Kinetica: AI is a ‘killer app’ for data analytics
2 May 2023, Blocks & Files

Kinetica Taps Dell for Hardware
12 June 2018, Finovate

provided by Google News

A Look at Valentina
18 April 2014, SitePoint

MySQL GUI Tools for Windows and Ubuntu/Linux: Top 8 free or open source
7 December 2018, H2S Media

provided by Google News

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