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DBMS > HBase vs. IRONdb vs. Vertica

System Properties Comparison HBase vs. IRONdb vs. Vertica

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Editorial information provided by DB-Engines
NameHBase  Xexclude from comparisonIRONdb  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 BigTableA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityCloud 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 storeTime Series DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score30.50
Rank#26  Overall
#2  Wide column stores
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitehbase.apache.orgwww.circonus.com/solutions/time-series-database/www.vertica.com
Technical documentationhbase.apache.org/­book.htmldocs.circonus.com/irondb/category/getting-startedvertica.com/­documentation
DeveloperApache Software Foundation infoApache top-level project, originally developed by PowersetCirconus LLC.OpenText infopreviously Micro Focus and Hewlett Packard
Initial release200820172005
Current release2.3.4, January 2021V0.10.20, January 201812.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache version 2commercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC and C++C++
Server operating systemsLinux
Unix
Windows infousing Cygwin
LinuxLinux
Data schemeschema-free, schema definition possibleschema-freeYes, 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, AVROyes infotext, numeric, histogramsyes
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 indexesnonoNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoSQL-like query language (Circonus Analytics Query Language: CAQL)Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJava API
RESTful HTTP API
Thrift
HTTP APIADO.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
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes infoCoprocessors in Javayes, in Luayes, PostgreSQL PL/pgSQL, with minor differences
Triggersyesnoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic, metric affinity per nodehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
configurable replication factor, datacenter awareMulti-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 methodsyesnono infoBi-directional Spark integration
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
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
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 & ABACnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
HBaseIRONdbVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
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...
» more
Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
» more

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More resources
HBaseIRONdbVertica infoOpenText™ Vertica™
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