DBMS > Apache IoTDB vs. FatDB vs. NuoDB vs. SQLite vs. Tibero

System Properties Comparison Apache IoTDB vs. FatDB vs. NuoDB vs. SQLite vs. Tibero

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Widely used embeddable, in-process RDBMS

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Time Series DBMS

Document store
Key-value store

Relational DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

iotdb.apache.org

www.3ds.com/nuodb-distributed-sql-database

www.sqlite.org

us.tmaxsoft.com/products/tibero

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

doc.nuodb.com

www.sqlite.org/docs.html

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Apache Software Foundation

FatCloud

Dassault Systèmes

originally NuoDB, Inc.

Dwayne Richard Hipp

TmaxSoft

Initial release

2018

2012

2013

2000

2003

Current release

1.1.0, April 2023

3.46.0 (23 May 2024), May 2024

6, April 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

limited edition free

Open Source

Public Domain

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

C++

C and Assembler

Server operating systems

All OS with a Java VM (>= 1.8)

Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

server-less

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

SQL-like query language

Via inetgration in SQL Server

yes

SQL-92 is not fully supported

yes

APIs and other access methods

JDBC
Native API

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

ADO.NET
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

via applications

Java, SQL

Persistent Stored Procedure (PSM)

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

data is dynamically stored/cached on the nodes where it is read/written

none

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

selectable replication factor

yes

Managed transparently by NuoDB

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

via file-system locks

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

Temporary table

yes

planned for next version

User concepts

Access control

yes

Can implement custom security layer via applications

Standard SQL roles/ privileges, Administrative Users

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.