DBMS > Firebird vs. Oracle NoSQL vs. SQLite vs. Teradata

System Properties Comparison Firebird vs. Oracle NoSQL vs. SQLite vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

Firebird is an open source RDBMS forked from Borland's InterBase

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Widely used embeddable, in-process RDBMS

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

www.firebirdsql.org

www.oracle.com/database/nosql/technologies/nosql

www.sqlite.org

www.teradata.com

Technical documentation

www.firebirdsql.org/en/reference-manuals

docs.oracle.com/en/database/other-databases/nosql-database/index.html

www.sqlite.org/docs.html

docs.teradata.com

Developer

Firebird Foundation

Oracle

Dwayne Richard Hipp

Teradata

Initial release

2000

As fork of Borland's InterBase

2011

2000

1984

Current release

5.0.0, January 2024

23.3, December 2023

3.45.3 (15 April 2024), April 2024

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Initial Developer's Public License

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

C and C++

Java

Server operating systems

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Linux
Solaris SPARC/x86

server-less

hosted
Linux

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

optional

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

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

yes

SQL 2016 + extensions

APIs and other access methods

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

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

Supported programming languages

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

C
C#
Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

PSQL

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate 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

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

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

off heap cache

yes

User concepts

Access control

Users with fine-grained authorization concept

Access rights for users and roles

fine grained access rights according to SQL-standard

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