DBMS > GBase vs. Netezza vs. Vertica

System Properties Comparison GBase vs. Netezza vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

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

Data warehouse and analytics appliance part of IBM PureSystems

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

Spatial DBMS
Time Series DBMS

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

Score	1.49
Rank	#142	Overall
	#64	Relational DBMS

Score	6.55
Rank	#52	Overall
	#32	Relational DBMS

Score	9.85
Rank	#42	Overall
	#26	Relational DBMS

Website

www.gbase.cn

www.ibm.com/products/netezza

www.vertica.com

Technical documentation

vertica.com/documentation

Developer

General Data Technology Co., Ltd.

IBM

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2004

2000

2005

Current release

GBase 8a, GBase 8s, GBase 8c

12.0.3, January 2023

License

Commercial or Open Source

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

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

Implementation language

C, Java, Python

C++

Server operating systems

Linux

included in appliance

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

Standard with numerous extensions

yes

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

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC
OLE DB

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

character-based, interactive, front-end utility

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

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

Server-side scripts

Stored procedures

user defined functions

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

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

yes

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.

User concepts

Access control

yes

Users with fine-grained authorization concept

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

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More resources

Recent citations in the news

Netezza and IBM Cloud Pak for Data: A knockout combo for tough data
15 January 2025, IBM

How to use Netezza Performance Server query data in Amazon Simple Storage Service (S3)
20 December 2024, IBM

How to migrate a large data warehouse from IBM Netezza to Amazon Redshift with no downtime
21 August 2019, Amazon Web Services

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26 October 2017, PR Newswire

Unify and share data across Netezza and watsonx.data for new generative AI applications
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25 October 2024, OpenText Blogs

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23 January 2025, Taboola.com

Fig. 4. Vertica Provides VPN and Firewall to Secure Its Database
20 November 2024, ResearchGate

New browser-based query editor for OpenText Core Analytics Database accelerates and simplifies querying your data
25 November 2024, OpenText Blogs

Querying a Vertica data source in Amazon Athena using the Athena Federated Query SDK
11 February 2021, Amazon Web Services

provided by Google News

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