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DBMS > Axibase vs. CrateDB vs. HBase vs. Kinetica

System Properties Comparison Axibase vs. CrateDB vs. HBase vs. Kinetica

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Editorial information provided by DB-Engines
NameAxibase  Xexclude from comparisonCrateDB  Xexclude from comparisonHBase  Xexclude from comparisonKinetica  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on HBase with integrated rule engine and visualizationDistributed Database based on LuceneWide-column store based on Apache Hadoop and on concepts of BigTableFully vectorized database across both GPUs and CPUs
Primary database modelTime Series DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Wide column storeRelational DBMS
Secondary database modelsRelational DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.29
Rank#292  Overall
#25  Time Series DBMS
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Websiteaxibase.com/­docs/­atsd/­financecratedb.comhbase.apache.orgwww.kinetica.com
Technical documentationcratedb.com/­docshbase.apache.org/­book.htmldocs.kinetica.com
DeveloperAxibase CorporationCrateApache Software Foundation infoApache top-level project, originally developed by PowersetKinetica
Initial release2013201320082012
Current release155852.3.4, January 20217.1, August 2021
License infoCommercial or Open Sourcecommercial infoCommunity Edition (single node) is free, Enterprise Edition (distributed) is paidOpen SourceOpen Source infoApache version 2commercial
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.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaJavaJavaC, C++
Server operating systemsLinuxAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
Unix
Windows infousing Cygwin
Linux
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-free, schema definition possibleyes
Typing infopredefined data types such as float or dateyes infoshort, integer, long, float, double, decimal, stringyesoptions to bring your own types, AVROyes
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.nononono
Secondary indexesnoyesnoyes
SQL infoSupport of SQLSQL-like query languageyes, but no triggers and constraints, and PostgreSQL compatibilitynoSQL-like DML and DDL statements
APIs and other access methodsJDBC
Proprietary protocol (Network API)
RESTful HTTP API
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
JDBC
ODBC
RESTful HTTP API
Supported programming languagesGo
Java
PHP
Python
R
Ruby
.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
C
C#
C++
Groovy
Java
PHP
Python
Scala
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesuser defined functions (Javascript)yes infoCoprocessors in Javauser defined functions
Triggersyesnoyesyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationConfigurable replication on table/partition-levelMulti-source replication
Source-replica replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate Consistency or Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infounique row identifiers can be used for implementing an optimistic concurrency control strategySingle row ACID (across millions of columns)no
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.noyesyes infoGPU vRAM or System RAM
User concepts infoAccess controlrights management via user accountsAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAccess rights for users and roles on table level
More information provided by the system vendor
AxibaseCrateDBHBaseKinetica
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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More resources
AxibaseCrateDBHBaseKinetica
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