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DBMS > Netezza vs. Teradata vs. TerminusDB vs. TimescaleDB vs. VelocityDB

System Properties Comparison Netezza vs. Teradata vs. TerminusDB vs. TimescaleDB vs. VelocityDB

Editorial information provided by DB-Engines
NameNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonTeradata  Xexclude from comparisonTerminusDB infoformer name was DataChemist  Xexclude from comparisonTimescaleDB  Xexclude from comparisonVelocityDB  Xexclude from comparison
DescriptionData warehouse and analytics appliance part of IBM PureSystemsA hybrid cloud data analytics software platform (Teradata Vantage)Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationshipsA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQLA .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)
Primary database modelRelational DBMSRelational DBMSGraph DBMSTime Series DBMSGraph DBMS
Object oriented DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
Time Series DBMS
Document store
RDF store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score9.06
Rank#46  Overall
#29  Relational DBMS
Score45.33
Rank#21  Overall
#15  Relational DBMS
Score0.17
Rank#325  Overall
#29  Graph DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Score0.05
Rank#358  Overall
#36  Graph DBMS
#16  Object oriented DBMS
Websitewww.ibm.com/­products/­netezzawww.teradata.comterminusdb.comwww.timescale.comvelocitydb.com
Technical documentationdocs.teradata.comterminusdb.github.io/­terminusdb/­#docs.timescale.comvelocitydb.com/­UserGuide
DeveloperIBMTeradataDataChemist Ltd.TimescaleVelocityDB Inc
Initial release20001984201820172011
Current releaseTeradata Vantage 1.0 MU2, January 201911.0.0, January 20232.15.0, May 20247.x
License infoCommercial or Open SourcecommercialcommercialOpen Source infoGPL V3Open Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageProlog, RustCC#
Server operating systemsLinux infoincluded in appliancehosted
Linux
LinuxLinux
OS X
Windows
Any that supports .NET
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data typesyes
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.yesnoyesno
Secondary indexesyesyes infoJoin-index to prejoin tables, aggregate index, sparse index, hash indexyesyes
SQL infoSupport of SQLyesyes infoSQL 2016 + extensionsSQL-like query language (WOQL)yes infofull PostgreSQL SQL syntaxno
APIs and other access methodsJDBC
ODBC
OLE DB
.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB
OWL
RESTful HTTP API
WOQL (Web Object Query Language)
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
.Net
Supported programming languagesC
C++
Fortran
Java
Lua
Perl
Python
R
C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby
JavaScript
Python
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
.Net
Server-side scripts infoStored proceduresyesyes infoUDFs, stored procedures, table functions in parallelyesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shellno
TriggersnoyesyesyesCallbacks are triggered when data changes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoHashingGraph Partitioningyes, across time and space (hash partitioning) attributesSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication
Source-replica replication
Journaling StreamsSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoin-memory journalingyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlUsers with fine-grained authorization conceptfine grained access rights according to SQL-standardRole-based access controlfine grained access rights according to SQL-standardBased on Windows Authentication

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More resources
Netezza infoAlso called PureData System for Analytics by IBMTeradataTerminusDB infoformer name was DataChemistTimescaleDBVelocityDB
DB-Engines blog posts

Teradata is the most popular data warehouse DBMS
2 April 2013, Paul Andlinger

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Recent citations in the news

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

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26 May 2024, AOL

Dimensional Fund Advisors LP Increases Holdings in Teradata Co. (NYSE:TDC)
26 May 2024, MarketBeat

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22 May 2024, Yahoo Finance

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TerminusDB Takes on Data Collaboration with a git-Like Approach
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Power IoT and time-series workloads with TimescaleDB for Azure Database for PostgreSQL
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Timescale Valuation Rockets to Over $1B with $110M Round, Marking the Explosive Rise of Time-Series Data
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