DBMS > 4D vs. Google Cloud Datastore vs. Microsoft Azure Cosmos DB vs. Teradata

System Properties Comparison 4D vs. Google Cloud Datastore vs. Microsoft Azure Cosmos DB vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Globally distributed, horizontally scalable, multi-model database service

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Relational 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	4.47
Rank	#76	Overall
	#12	Document stores

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

Website

www.4d.com

cloud.google.com/datastore

azure.microsoft.com/services/cosmos-db

www.teradata.com

Technical documentation

developer.4d.com

cloud.google.com/datastore/docs

learn.microsoft.com/azure/cosmos-db

docs.teradata.com

Developer

4D, Inc

Google

Microsoft

Teradata

Initial release

1984

2008

2014

1984

Current release

v20, April 2023

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

Providers of DBaaS offerings, please contact us to be listed.

Server operating systems

OS X
Windows

hosted

hosted
Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL-like query language (GQL)

SQL-like query language

yes

SQL 2016 + extensions

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

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

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

Supported programming languages

4D proprietary IDE
PHP

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.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

Server-side scripts

Stored procedures

yes

using Google App Engine

JavaScript

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Callbacks using the Google Apps Engine

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

Multi-source replication using Paxos

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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