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DBMS > BigObject vs. CrateDB vs. HBase vs. Transwarp KunDB

System Properties Comparison BigObject vs. CrateDB vs. HBase vs. Transwarp KunDB

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Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisonCrateDB  Xexclude from comparisonHBase  Xexclude from comparisonTranswarp KunDB  Xexclude from comparison
DescriptionAnalytic DBMS for real-time computations and queriesDistributed Database based on LuceneWide-column store based on Apache Hadoop and on concepts of BigTableOLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Wide column storeRelational DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.19
Rank#329  Overall
#146  Relational DBMS
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#9  Vector DBMS
Score27.97
Rank#26  Overall
#2  Wide column stores
Score0.14
Rank#341  Overall
#149  Relational DBMS
Websitebigobject.iocratedb.comhbase.apache.orgwww.transwarp.cn/­en/­product/­kundb
Technical documentationdocs.bigobject.iocratedb.com/­docshbase.apache.org/­book.html
DeveloperBigObject, Inc.CrateApache Software Foundation infoApache top-level project, originally developed by PowersetTranswarp
Initial release201520132008
Current release2.3.4, January 2021
License infoCommercial or Open Sourcecommercial infofree community edition availableOpen 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 languageJavaJava
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
Unix
Windows infousing Cygwin
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-free, schema definition possibleyes
Typing infopredefined data types such as float or dateyesyesoptions 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.nonono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyes, but no triggers and constraints, and PostgreSQL compatibilitynoyes
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
Supported programming languages.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
Server-side scripts infoStored proceduresLuauser defined functions (Javascript)yes infoCoprocessors in Javayes
Triggersnonoyesyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardinghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneConfigurable replication on table/partition-levelMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneEventual Consistency
Read-after-write consistency on record level
Immediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesnonoyes
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)ACID
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yesyesyes
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.yesnoyes
User concepts infoAccess controlnorights management via user accountsAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACyes
More information provided by the system vendor
BigObjectCrateDBHBaseTranswarp KunDB
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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BigObjectCrateDBHBaseTranswarp KunDB
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