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DBMS > HBase vs. Hive vs. IRONdb vs. RDF4J vs. Vertica

System Properties Comparison HBase vs. Hive vs. IRONdb vs. RDF4J vs. Vertica

Editorial information provided by DB-Engines
NameHBase  Xexclude from comparisonHive  Xexclude from comparisonIRONdb  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionWide-column store based on Apache Hadoop and on concepts of BigTabledata warehouse software for querying and managing large distributed datasets, built on HadoopA 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.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 modelWide column storeRelational DBMSTime Series DBMSRDF storeRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score31.25
Rank#26  Overall
#2  Wide column stores
Score62.59
Rank#18  Overall
#12  Relational DBMS
Score0.71
Rank#231  Overall
#9  RDF stores
Score11.40
Rank#43  Overall
#27  Relational DBMS
Websitehbase.apache.orghive.apache.orgwww.circonus.com/solutions/time-series-database/rdf4j.orgwww.vertica.com
Technical documentationhbase.apache.org/­book.htmlcwiki.apache.org/­confluence/­display/­Hive/­Homedocs.circonus.com/irondb/category/getting-startedrdf4j.org/­documentationvertica.com/­documentation
DeveloperApache Software Foundation infoApache top-level project, originally developed by PowersetApache Software Foundation infoinitially developed by FacebookCirconus LLC.Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.OpenText infopreviously Micro Focus and Hewlett Packard
Initial release20082012201720042005
Current release2.3.4, January 20213.1.3, April 2022V0.10.20, January 201812.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache version 2Open Source infoApache Version 2commercialOpen Source infoEclipse Distribution License (EDL), v1.0.commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonononono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC and C++JavaC++
Server operating systemsLinux
Unix
Windows infousing Cygwin
All OS with a Java VMLinuxLinux
OS X
Unix
Windows
Linux
Data schemeschema-free, schema definition possibleyesschema-freeyes infoRDF SchemasYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateoptions to bring your own types, AVROyesyes 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.nonono
Secondary indexesnoyesnoyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoSQL-like DML and DDL statementsSQL-like query language (Circonus Analytics Query Language: CAQL)noFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJava API
RESTful HTTP API
Thrift
JDBC
ODBC
Thrift
HTTP APIJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC
C#
C++
Groovy
Java
PHP
Python
Scala
C++
Java
PHP
Python
.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++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes infoCoprocessors in Javayes infouser defined functions and integration of map-reduceyes, in Luayesyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyesnonoyesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingShardingAutomatic, metric affinity per nodenonehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
selectable replication factorconfigurable replication factor, datacenter awarenoneMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infoquery execution via MapReducenonono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual ConsistencyImmediate consistency per node, eventual consistency across nodesImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataSingle row ACID (across millions of columns)nonoACID infoIsolation support depends on the API usedACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes 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.yesnono
User concepts infoAccess controlAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAccess rights for users, groups and rolesnonofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
HBaseHiveIRONdbRDF4J infoformerly known as SesameVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
HBaseHiveIRONdbRDF4J infoformerly known as SesameVertica infoOpenText™ Vertica™
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