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DBMS > HBase vs. IRONdb vs. RDF4J vs. Teradata

System Properties Comparison HBase vs. IRONdb vs. RDF4J vs. Teradata

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Editorial information provided by DB-Engines
NameHBase  Xexclude from comparisonIRONdb  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonTeradata  Xexclude from comparison
DescriptionWide-column store based on Apache Hadoop and on concepts of BigTableA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.A hybrid cloud data analytics software platform (Teradata Vantage)
Primary database modelWide column storeTime Series DBMSRDF storeRelational DBMS
Secondary database modelsDocument store
Graph DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score49.11
Rank#22  Overall
#2  Wide column stores
Score0.06
Rank#317  Overall
#28  Time Series DBMS
Score0.50
Rank#220  Overall
#9  RDF stores
Score76.78
Rank#14  Overall
#9  Relational DBMS
Websitehbase.apache.orgwww.irondb.iordf4j.orgwww.teradata.com
Technical documentationhbase.apache.orgwww.irondb.io/­docsrdf4j.org/­documentationdocs.teradata.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by PowersetCirconus LLC.Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.Teradata
Initial release2008201720041984
Current release2.2.0, June 2019V0.10.20, January 2018Teradata Vantage 1.0 MU2, January 2019
License infoCommercial or Open SourceOpen Source infoApache version 2commercialOpen Source infoEclipse Distribution License (EDL), v1.0.commercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC and C++Java
Server operating systemsLinux
Unix
Windows infousing Cygwin
LinuxLinux
OS X
Unix
Windows
hosted
Linux
Data schemeschema-free, schema definition possibleschema-freeyes infoRDF Schemasyes
Typing infopredefined data types such as float or dateoptions to bring your own types, AVROyes infotext, numeric, histogramsyesyes
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 indexesnonoyesyes infoJoin-index to prejoin tables, aggregate index, sparse index, hash index
SQL infoSupport of SQLnoSQL-like query language (Circonus Analytics Query Language: CAQL)noyes infoSQL 2016 + extensions
APIs and other access methodsJava API
RESTful HTTP API
Thrift
HTTP APIJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB
Supported programming languagesC
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Java
PHP
Python
C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby
Server-side scripts infoStored proceduresyes infoCoprocessors in Javayes, in Luayesyes infoUDFs, stored procedures, table functions in parallel
Triggersyesnoyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic, metric affinity per nodenoneSharding infoHashing
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
configurable replication factor, datacenter awarenoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate consistency per node, eventual consistency across nodesImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataSingle row ACID (across millions of columns)noACID infoIsolation support depends on the API usedACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoin-memory storage is supported as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACnonofine grained access rights according to SQL-standard
More information provided by the system vendor
HBaseIRONdbRDF4J infoformerly known as SesameTeradata
Specific characteristicsIRONdb is a highly available, distributed Time Series Database. It can support dozens...
» more
Competitive advantagesUnmatched Scalability IRONdb is unique among TSDBs in that it does not use a consensus...
» more
Typical application scenariosReal Systems Monitoring Monitor your systems infrastructure in real time across thousands...
» more
Key customersIRONdb serves the needs of the world's largest Time Series Database customers. One...
» more
Market metricsOnly TSDB capable of scaling to billions of metrics. Only TSDB to scale to dozens...
» more
Licensing and pricing modelsIRONdb is licensed under a subscription model based on the number of active metrics...
» more

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More resources
HBaseIRONdbRDF4J infoformerly known as SesameTeradata
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