DBMS > Amazon Redshift vs. Faircom DB vs. ObjectBox vs. SQLite vs. TigerGraph

System Properties Comparison Amazon Redshift vs. Faircom DB vs. ObjectBox vs. SQLite vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Extremely fast embedded database for small devices, IoT and Mobile

Widely used embeddable, in-process RDBMS

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Object oriented DBMS

Relational DBMS

Graph DBMS

Secondary database models

Time Series DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	0.24
Rank	#311	Overall
	#44	Key-value stores
	#140	Relational DBMS

Score	1.22
Rank	#172	Overall
	#5	Object oriented DBMS

Score	116.01
Rank	#10	Overall
	#7	Relational DBMS

Score	1.83
Rank	#141	Overall
	#13	Graph DBMS

Website

aws.amazon.com/redshift

www.faircom.com/products/faircom-db

objectbox.io

www.sqlite.org

www.tigergraph.com

Technical documentation

docs.aws.amazon.com/redshift

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

docs.objectbox.io

www.sqlite.org/docs.html

docs.tigergraph.com

Developer

Amazon (based on PostgreSQL)

FairCom Corporation

ObjectBox Limited

Dwayne Richard Hipp

Initial release

2012

1979

2017

2000

2017

Current release

V12, November 2020

3.45.2 (12 March 2024), March 2024

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache License 2.0

Open Source

Public Domain

commercial

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

ANSI C, C++

C and C++

C++

Server operating systems

hosted

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

easily portable to other OSs

Android
iOS
Linux
macOS
Windows

server-less

Linux

Data scheme

yes

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

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes, ANSI SQL with proprietary extensions

yes

SQL-92 is not fully supported

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC

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

Proprietary native API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

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++
Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

online/offline synchronization between client and server

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Relationships in graphs

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

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

yes

Role-based access control

More information provided by the system vendor

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15 February 2024

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14 November 2023

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