DBMS > Blueflood vs. Faircom DB vs. JaguarDB vs. ObjectBox vs. Teradata

System Properties Comparison Blueflood vs. Faircom DB vs. JaguarDB vs. ObjectBox vs. Teradata

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Performant, highly scalable DBMS for AI and IoT applications

Extremely fast embedded database for small devices, IoT and Mobile

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Time Series DBMS

Key-value store
Relational DBMS

Key-value store
Vector DBMS

Object oriented DBMS

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

blueflood.io

www.faircom.com/products/faircom-db

www.jaguardb.com

objectbox.io

www.teradata.com

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

www.jaguardb.com/support.html

docs.objectbox.io

docs.teradata.com

Developer

Rackspace

FairCom Corporation

DataJaguar, Inc.

ObjectBox Limited

Teradata

Initial release

2013

1979

2015

2017

1984

Current release

V12, November 2020

3.3 July 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Restricted, free version available

Open Source

GPL V3.0

Open Source

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

Java

ANSI C, C++

C++

the server part. Clients available in other languages

C and C++

Server operating systems

Linux
OS X

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux

Android
iOS
Linux
macOS
Windows

hosted
Linux

Data scheme

predefined scheme

schema free, schema optional, schema required, partial schema,

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL 2016 + extensions

APIs and other access methods

HTTP REST

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

JDBC
ODBC

Proprietary native API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source replication

online/offline synchronization between client and server

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

rights management via user accounts

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

The first On-Device Vector Database: ObjectBox 4.0
16 May 2024

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

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