DBMS > 4D vs. Atos Standard Common Repository vs. Google BigQuery vs. HEAVY.AI vs. OrientDB

System Properties Comparison 4D vs. Atos Standard Common Repository vs. Google BigQuery vs. HEAVY.AI vs. OrientDB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Application development environment with integrated database management system

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Large scale data warehouse service with append-only tables

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store

Secondary database models

Spatial DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Website

www.4d.com

atos.net/en/convergence-creators/portfolio/standard-common-repository

cloud.google.com/bigquery

github.com/heavyai/heavydb
www.heavy.ai

orientdb.org

Technical documentation

developer.4d.com

cloud.google.com/bigquery/docs

docs.heavy.ai

www.orientdb.com/docs/last/index.html

Developer

4D, Inc

Atos Convergence Creators

Google

HEAVY.AI, Inc.

OrientDB LTD; CallidusCloud; SAP

Initial release

1984

2016

2010

2016

2010

Current release

v20, April 2023

1703

5.10, January 2022

3.2.29, March 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache version 2

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++ and CUDA

Java

Server operating systems

OS X
Windows

Linux

hosted

Linux

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

Schema and schema-less with LDAP views

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

Typing

predefined data types such as float or date

yes

optional

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

SQL

Support of SQL

yes

close to SQL 92

yes

SQL-like query language, no joins

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

LDAP

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

4D proprietary IDE
PHP

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Java, Javascript

Triggers

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

cell division

none

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

Atomic execution of specific operations

Since BigQuery is designed for querying 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

LDAP bind authentication

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users and roles; record level security configurable

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