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DBMS > Databricks vs. HBase vs. Sequoiadb vs. Tkrzw vs. YDB

System Properties Comparison Databricks vs. HBase vs. Sequoiadb vs. Tkrzw vs. YDB

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHBase  Xexclude from comparisonSequoiadb  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparisonYDB  Xexclude from comparison
DescriptionThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Wide-column store based on Apache Hadoop and on concepts of BigTableNewSQL database with distributed OLTP and SQLA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto CabinetA distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API
Primary database modelDocument store
Relational DBMS
Wide column storeDocument store
Relational DBMS
Key-value storeDocument store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Score0.45
Rank#261  Overall
#41  Document stores
#122  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Score0.35
Rank#278  Overall
#43  Document stores
#127  Relational DBMS
Websitewww.databricks.comhbase.apache.orgwww.sequoiadb.comdbmx.net/­tkrzwgithub.com/­ydb-platform/­ydb
ydb.tech
Technical documentationdocs.databricks.comhbase.apache.org/­book.htmlwww.sequoiadb.com/­en/­index.php?m=Files&a=indexydb.tech/­en/­docs
DeveloperDatabricksApache Software Foundation infoApache top-level project, originally developed by PowersetSequoiadb Ltd.Mikio HirabayashiYandex
Initial release20132008201320202019
Current release2.3.4, January 20210.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache version 2Open Source infoServer: AGPL; Client: Apache V2Open Source infoApache Version 2.0Open Source infoApache 2.0; commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++C++
Server operating systemshostedLinux
Unix
Windows infousing Cygwin
LinuxLinux
macOS
Linux
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-free, schema definition possibleschema-freeschema-freeFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateoptions to bring your own types, AVROyes infooid, date, timestamp, binary, regexnoyes
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.yesnononono
Secondary indexesyesnoyesyes
SQL infoSupport of SQLwith Databricks SQLnoSQL-like query languagenoSQL-like query language (YQL)
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
proprietary protocol using JSONRESTful HTTP API (DynamoDB compatible)
Supported programming languagesPython
R
Scala
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
C++
Java
PHP
Python
C++
Java
Python
Ruby
Go
Java
JavaScript (Node.js)
PHP
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infoCoprocessors in JavaJavaScriptnono
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
Source-replica replicationnoneActive-passive shard replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual ConsistencyEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDSingle row ACID (across millions of columns)Document is locked during a transactionACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes infousing specific database classesno
User concepts infoAccess controlAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACsimple password-based access controlnoAccess rights defined for Yandex Cloud users
More information provided by the system vendor
DatabricksHBaseSequoiadbTkrzw infoSuccessor of Tokyo Cabinet and Kyoto CabinetYDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksHBaseSequoiadbTkrzw infoSuccessor of Tokyo Cabinet and Kyoto CabinetYDB
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