DBMS > Apache Drill vs. Cubrid vs. jBASE vs. Netezza

System Properties Comparison Apache Drill vs. Cubrid vs. jBASE vs. Netezza

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

A robust multi-value DBMS comprising development tools and middleware

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

Document store
Relational DBMS

Relational DBMS

Multivalue DBMS

Relational DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Website

drill.apache.org

cubrid.com (korean)
cubrid.org (english)

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.ibm.com/products/netezza

Technical documentation

drill.apache.org/docs

cubrid.org/manuals

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

Developer

Apache Software Foundation

CUBRID Corporation, CUBRID Foundation

Rocket Software (formerly Zumasys)

IBM

Initial release

2012

2008

1991

2000

Current release

1.20.3, January 2023

11.0, January 2021

5.7

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++, Java

Server operating systems

Linux
OS X
Windows

Linux
Windows

AIX
Linux
Windows

Linux

included in appliance

Data scheme

schema-free

yes

schema-free

yes

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

SQL SELECT statement is SQL:2003 compliant

yes

Embedded SQL for jBASE in BASIC

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

JDBC
ODBC
OLE DB

Supported programming languages

C++

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

.Net
Basic
Jabbascript
Java

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

user defined functions

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

Users with fine-grained authorization concept

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More resources

Recent citations in the news

Apache Drill case study: A tutorial on processing CSV files
9 June 2016, TheServerSide.com

Analyse Kafka messages with SQL queries using Apache Drill
23 September 2019, Towards Data Science

Apache Drill Poised to Crack Tough Data Challenges
19 May 2015, Datanami

Apache Drill Eliminates ETL, Data Transformation for MapR Database
11 April 2016, The New Stack

Apache Drill improves big data SQL query engine
31 August 2021, TechTarget

provided by Google News

NHN Willing to Be More Open
24 November 2008, 코리아타임스

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24 August 2009, Finextra

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IBM announces availability of the high-performance, cloud-native Netezza Performance Server as a Service on AWS
11 July 2023, IBM

AWS and IBM Netezza come out in support of Iceberg in table format face-off
1 August 2023, The Register

Migrating your Netezza data warehouse to Amazon Redshift | Amazon Web Services
27 May 2020, AWS Blog

IBM Brings Back a Netezza, Attacks Yellowbrick
29 June 2020, Datanami

Netezza Performance Server
12 August 2020, IBM

provided by Google News

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