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

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

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonFeatureBase  Xexclude from comparisonHBase  Xexclude from comparisonSequoiadb  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  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.Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.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 Cabinet
Primary database modelDocument store
Relational DBMS
Relational DBMSWide column storeDocument store
Relational DBMS
Key-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.22
Rank#309  Overall
#139  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
Websitewww.databricks.comwww.featurebase.comhbase.apache.orgwww.sequoiadb.comdbmx.net/­tkrzw
Technical documentationdocs.databricks.comdocs.featurebase.comhbase.apache.org/­book.htmlwww.sequoiadb.com/­en/­index.php?m=Files&a=index
DeveloperDatabricksMolecula and Pilosa Open Source ContributorsApache Software Foundation infoApache top-level project, originally developed by PowersetSequoiadb Ltd.Mikio Hirabayashi
Initial release20132017200820132020
Current release2022, May 20222.3.4, January 20210.9.3, August 2020
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache version 2Open Source infoServer: AGPL; Client: Apache V2Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoJavaC++C++
Server operating systemshostedLinux
macOS
Linux
Unix
Windows infousing Cygwin
LinuxLinux
macOS
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-free, schema definition possibleschema-freeschema-free
Typing infopredefined data types such as float or dateyesoptions to bring your own types, AVROyes infooid, date, timestamp, binary, regexno
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 indexesyesnonoyes
SQL infoSupport of SQLwith Databricks SQLSQL queriesnoSQL-like query languageno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC
JDBC
Kafka Connector
ODBC
Java API
RESTful HTTP API
Thrift
proprietary protocol using JSON
Supported programming languagesPython
R
Scala
Java
Python
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
C++
Java
PHP
Python
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infoCoprocessors in JavaJavaScriptno
Triggersnoyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesyesMulti-source replication
Source-replica replication
Source-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesSingle row ACID (across millions of columns)Document is locked during a transaction
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes, using Linux fsyncyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesnoyes infousing specific database classes
User concepts infoAccess controlAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACsimple password-based access controlno
More information provided by the system vendor
DatabricksFeatureBaseHBaseSequoiadbTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksFeatureBaseHBaseSequoiadbTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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