DBMS > 4D vs. Google BigQuery vs. Kinetica vs. OpenMLDB vs. TimesTen

System Properties Comparison 4D vs. Google BigQuery vs. Kinetica vs. OpenMLDB vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

An open-source machine learning database that provides a feature platform for training and inference

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	2.13
Rank	#114	Overall
	#54	Relational DBMS

Score	50.58
Rank	#19	Overall
	#13	Relational DBMS

Score	0.42
Rank	#257	Overall
	#120	Relational DBMS

Score	0.08
Rank	#344	Overall
	#30	Time Series DBMS

Score	1.23
Rank	#163	Overall
	#74	Relational DBMS

Website

www.4d.com

cloud.google.com/bigquery

www.kinetica.com

openmldb.ai

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

developer.4d.com

cloud.google.com/bigquery/docs

docs.kinetica.com

openmldb.ai/docs/zh/main

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

4D, Inc

Google

Kinetica

4 Paradigm Inc.

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

1984

2010

2012

2020

1998

Current release

v20, April 2023

7.1, August 2021

2024-2 February 2024

Release 22.1

License

Commercial or Open Source

commercial

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.

Implementation language

C, C++

C++, Java, Scala

Server operating systems

OS X
Windows

hosted

Linux

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

yes

Fixed schema

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

SQL

Support of SQL

yes

close to SQL 92

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

JDBC
SQLAlchemy

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

4D proprietary IDE
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

C++
Go
Java
Python
Scala

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

user defined functions

PL/SQL

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Users and groups

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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