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DBMS > Blueflood vs. GBase vs. Kinetica vs. Vertica

System Properties Comparison Blueflood vs. GBase vs. Kinetica vs. Vertica

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Editorial information provided by DB-Engines
NameBlueflood  Xexclude from comparisonGBase  Xexclude from comparisonKinetica  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on CassandraWidely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.Fully vectorized database across both GPUs and CPUsCloud 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 modelTime Series DBMSRelational DBMSRelational DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#346  Overall
#33  Time Series DBMS
Score1.05
Rank#186  Overall
#86  Relational DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websiteblueflood.iowww.gbase.cnwww.kinetica.comwww.vertica.com
Technical documentationgithub.com/­rax-maas/­blueflood/­wikidocs.kinetica.comvertica.com/­documentation
DeveloperRackspaceGeneral Data Technology Co., Ltd.KineticaOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2013200420122005
Current releaseGBase 8a, GBase 8s, GBase 8c7.1, August 202112.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono 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, Java, PythonC, C++C++
Server operating systemsLinux
OS X
LinuxLinuxLinux
Data schemepredefined schemeyesyesYes, 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 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.noyesnono
Secondary indexesnoyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoStandard with numerous extensionsSQL-like DML and DDL statementsFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsHTTP RESTADO.NET
C API
JDBC
ODBC
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC#C++
Java
JavaScript (Node.js)
Python
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnouser defined functionsuser defined functionsyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyesyes infotriggers when inserted values for one or more columns fall within a specified rangeyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on Cassandrahorizontal partitioning (by range, list and hash) and vertical partitioningShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on CassandrayesSource-replica replicationMulti-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 methodsnonono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID
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.noyes infoGPU vRAM or System RAMno
User concepts infoAccess controlnoyesAccess rights for users and roles on table levelfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
BluefloodGBaseKineticaVertica 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
BluefloodGBaseKineticaVertica infoOpenText™ Vertica™
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