DBMS > 4D vs. GridGain vs. Microsoft Azure Cosmos DB vs. Teradata vs. TigerGraph

System Properties Comparison 4D vs. GridGain vs. Microsoft Azure Cosmos DB vs. Teradata vs. TigerGraph

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

Globally distributed, horizontally scalable, multi-model database service

A hybrid cloud data analytics software platform (Teradata Vantage)

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

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

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	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

www.4d.com

www.gridgain.com

azure.microsoft.com/services/cosmos-db

www.teradata.com

www.tigergraph.com

Technical documentation

developer.4d.com

www.gridgain.com/docs/index.html

learn.microsoft.com/azure/cosmos-db

docs.teradata.com

docs.tigergraph.com

Developer

4D, Inc

GridGain Systems, Inc.

Microsoft

Teradata

Initial release

1984

2007

2014

1984

2017

Current release

v20, April 2023

GridGain 8.5.1

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

C++

Server operating systems

OS X
Windows

Linux
OS X
Solaris
Windows

hosted

hosted
Linux

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

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

SQL-like query language

yes

SQL 2016 + extensions

SQL-like query language (GSQL)

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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

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

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

Supported programming languages

4D proprietary IDE
PHP

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

C++
Java

Server-side scripts

Stored procedures

yes

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

JavaScript

yes

UDFs, stored procedures, table functions in parallel

yes

Triggers

yes

yes (cache interceptors and events)

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes (replicated cache)

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

Security Hooks for custom implementations

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

Role-based access control

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