DBMS > 4D vs. Amazon Redshift vs. GigaSpaces vs. TigerGraph

System Properties Comparison 4D vs. Amazon Redshift vs. GigaSpaces vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Large scale data warehouse service for use with business intelligence tools

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

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

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Graph DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

www.4d.com

aws.amazon.com/redshift

www.gigaspaces.com

www.tigergraph.com

Technical documentation

developer.4d.com

docs.aws.amazon.com/redshift

docs.gigaspaces.com/latest/landing.html

docs.tigergraph.com

Developer

4D, Inc

Amazon (based on PostgreSQL)

Gigaspaces Technologies

Initial release

1984

2012

2000

2017

Current release

v20, April 2023

15.5, September 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

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

Java, C++, .Net

C++

Server operating systems

OS X
Windows

hosted

Linux
macOS
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

XML can be used for describing objects metadata

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

close to SQL 92

yes

does not fully support an SQL-standard

SQL-99 for query and DML statements

SQL-like query language (GSQL)

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

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

Supported programming languages

4D proprietary IDE
PHP

All languages supporting JDBC/ODBC

.Net
C++
Java
Python
Scala

C++
Java

Server-side scripts

Stored procedures

yes

user defined functions

in Python

yes

Triggers

yes

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

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

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

User concepts

Access control

Users and groups

fine grained access rights according to SQL-standard

Role-based access control

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