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DBMS > Netezza vs. Spark SQL vs. Trafodion vs. Virtuoso

System Properties Comparison Netezza vs. Spark SQL vs. Trafodion vs. Virtuoso

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Editorial information provided by DB-Engines
NameNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonSpark SQL  Xexclude from comparisonTrafodion  Xexclude from comparisonVirtuoso  Xexclude from comparison
Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.
DescriptionData warehouse and analytics appliance part of IBM PureSystemsSpark SQL is a component on top of 'Spark Core' for structured data processingTransactional SQL-on-Hadoop DBMSVirtuoso is a multi-model hybrid-RDBMS that supports management of data represented as relational tables and/or property graphs
Primary database modelRelational DBMSRelational DBMSRelational DBMSDocument store
Graph DBMS
Native XML DBMS
Relational DBMS
RDF store
Search engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score8.59
Rank#45  Overall
#29  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score4.27
Rank#73  Overall
#13  Document stores
#4  Graph DBMS
#2  Native XML DBMS
#39  Relational DBMS
#2  RDF stores
#9  Search engines
Websitewww.ibm.com/­products/­netezzaspark.apache.org/­sqltrafodion.apache.orgvirtuoso.openlinksw.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmltrafodion.apache.org/­documentation.htmldocs.openlinksw.com/­virtuoso
DeveloperIBMApache Software FoundationApache Software Foundation, originally developed by HPOpenLink Software
Initial release2000201420141998
Current release3.5.0 ( 2.13), September 20232.3.0, February 20197.2.11, September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoApache 2.0Open Source infoGPLv2, extended commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC++, JavaC
Server operating systemsLinux infoincluded in applianceLinux
OS X
Windows
LinuxAIX
FreeBSD
HP-UX
Linux
OS X
Solaris
Windows
Data schemeyesyesyesyes infoSQL - Standard relational schema
RDF - Quad (S, P, O, G) or Triple (S, P, O)
XML - DTD, XML Schema
DAV - freeform filesystem objects, plus User Defined Types a/k/a Dynamic Extension Type
Typing infopredefined data types such as float or dateyesyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nonoyes
Secondary indexesyesnoyesyes
SQL infoSupport of SQLyesSQL-like DML and DDL statementsyesyes infoSQL-92, SQL-200x, SQL-3, SQLX
APIs and other access methodsJDBC
ODBC
OLE DB
JDBC
ODBC
ADO.NET
JDBC
ODBC
ADO.NET
GeoSPARQL
HTTP API
JDBC
Jena RDF API
ODBC
OLE DB
RDF4J API
RESTful HTTP API
Sesame REST HTTP Protocol
SOAP webservices
SPARQL 1.1
WebDAV
XPath
XQuery
XSLT
Supported programming languagesC
C++
Fortran
Java
Lua
Perl
Python
R
Java
Python
R
Scala
All languages supporting JDBC/ODBC/ADO.Net.Net
C
C#
C++
Java
JavaScript
Perl
PHP
Python
Ruby
Visual Basic
Server-side scripts infoStored proceduresyesnoJava Stored Proceduresyes infoVirtuoso PL
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreShardingyes
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnoneyes, via HBaseChain, star, and bi-directional replication
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infovia user defined functions and HBaseyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlUsers with fine-grained authorization conceptnofine grained access rights according to SQL-standardFine-grained Attribute-Based Access Control (ABAC) in addition to typical coarse-grained Role-Based Access Control (RBAC) according to SQL-standard. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)
More information provided by the system vendor
Netezza infoAlso called PureData System for Analytics by IBMSpark SQLTrafodionVirtuoso
Specific characteristicsVirtuoso is a modern multi-model RDBMS for managing data represented as tabular relations...
» more
Competitive advantagesPerformance & Scale โ€” as exemplified by DBpedia and the LOD Cloud it spawned, i.e.,...
» more
Typical application scenariosUsed for โ€” Analytics/BI Conceptual Data Virtualization Enterprise Knowledge Graphs...
» more
Key customersBroad use across enterprises and governments including โ€” European Union (EU) US Government...
» more
Market metricsLargest installed-base โ€‹of Multi-Model RDBMS for AI-friendly Knowledge Graphs Platform...
» more
Licensing and pricing modelsAvailable in both Commercial Enterprise and Open Source (GPL v2) Editions Feature...
» more

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More resources
Netezza infoAlso called PureData System for Analytics by IBMSpark SQLTrafodionVirtuoso
Recent citations in the news

Unify and share data across Netezza and watsonx.data for new generative AI applications
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