DBMS > 4D vs. GBase vs. Microsoft SQL Server vs. Vertica

System Properties Comparison 4D vs. GBase vs. Microsoft SQL Server vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Microsofts flagship relational DBMS

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Column oriented

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	2.27
Rank	#112	Overall
	#54	Relational DBMS

Score	1.11
Rank	#173	Overall
	#78	Relational DBMS

Score	802.09
Rank	#3	Overall
	#3	Relational DBMS

Score	10.02
Rank	#42	Overall
	#26	Relational DBMS

Website

www.4d.com

www.gbase.cn

www.microsoft.com/en-us/sql-server

www.vertica.com

Technical documentation

developer.4d.com

learn.microsoft.com/en-US/sql/sql-server

vertica.com/documentation

Developer

4D, Inc

General Data Technology Co., Ltd.

Microsoft

OpenText

previously Micro Focus and Hewlett Packard

Initial release

1984

2004

1989

2005

Current release

v20, April 2023

GBase 8a, GBase 8s, GBase 8c

SQL Server 2022, November 2022

12.0.3, January 2023

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, Java, Python

C++

Server operating systems

OS X
Windows

Linux

Linux
Windows

Linux

Data scheme

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

close to SQL 92

Standard with numerous extensions

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

ADO.NET
C API
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

4D proprietary IDE
PHP

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by range, list and hash) and vertical partitioning

tables can be distributed across several files (horizontal partitioning); sharding through federation

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

yes, but depending on the SQL-Server Edition

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

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

User concepts

Access control

Users and groups

yes

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

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More resources

DB-Engines blog posts

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Conferences, events and webinars

more DBMS events

PASS Data Community Summit
Seattle, Washington, 4-8 November 2024

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