DBMS > 4D vs. GridGain vs. Sadas Engine vs. Teradata

System Properties Comparison 4D vs. GridGain vs. Sadas Engine vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

GridGain is an in-memory computing platform, built on Apache Ignite

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

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	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

Technical documentation

developer.4d.com

www.gridgain.com/docs/index.html

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.teradata.com

Developer

4D, Inc

GridGain Systems, Inc.

SADAS s.r.l.

Teradata

Initial release

1984

2007

2006

1984

Current release

v20, April 2023

GridGain 8.5.1

8.0

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

free trial version available

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++, .Net

C++

Server operating systems

OS X
Windows

Linux
OS X
Solaris
Windows

AIX
Linux
Windows

hosted
Linux

Data scheme

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

Secondary indexes

yes

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

SQL

Support of SQL

yes

close to SQL 92

ANSI-99 for query and DML statements, subset of DDL

yes

SQL 2016 + extensions

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
Proprietary protocol

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

Supported programming languages

4D proprietary IDE
PHP

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes (replicated cache)

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

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

managed by 'Learn by Usage'

yes

User concepts

Access control

Users and groups

Security Hooks for custom implementations

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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Teradata is the most popular data warehouse DBMS
2 April 2013, Paul Andlinger

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Recent citations in the news

GridGain to Sponsor and Speak at Three Key Industry Events in May 2024
2 May 2024, PR Newswire

GridGain's 2023 Growth Positions Company for Strong 2024
25 January 2024, Datanami

GridGain Named in the 2023 Gartner® Market Guide for Event Stream Processing
22 August 2023, GlobeNewswire

GridGain Announces Call for Speakers for Virtual Apache Ignite Summit 2024
8 February 2024, PR Newswire

GridGain Releases Platform v8.9 for High-Speed Analytics Across Disparate Data Workloads
12 October 2023, Datanami

provided by Google News

Lakehouse dam breaks after departure of long-time Teradata CTO
1 May 2024, The Register

Teradata adds support for Apache Iceberg, Delta Lake tables
30 April 2024, InfoWorld

Teradata expands AWS collaboration for cloud analytics By Investing.com
2 May 2024, Investing.com

Unify Analytics Leveraging Amazon Athena and Teradata for Robust Query Federation | Amazon Web Services
23 April 2024, AWS Blog

Teradata expands strategic collaboration agreement with AWS to further support customers on cloud modernisation ...
3 May 2024, iTWire

provided by Google News

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