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

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

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHBase  Xexclude from comparisonIRONdb  Xexclude from comparisonSequoiadb  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
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 BigTableA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityNewSQL 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
Wide column storeTime Series DBMSDocument 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
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.comhbase.apache.orgwww.circonus.com/solutions/time-series-database/www.sequoiadb.comdbmx.net/­tkrzw
Technical documentationdocs.databricks.comhbase.apache.org/­book.htmldocs.circonus.com/irondb/category/getting-startedwww.sequoiadb.com/­en/­index.php?m=Files&a=index
DeveloperDatabricksApache Software Foundation infoApache top-level project, originally developed by PowersetCirconus LLC.Sequoiadb Ltd.Mikio Hirabayashi
Initial release20132008201720132020
Current release2.3.4, January 2021V0.10.20, January 20180.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache version 2commercialOpen 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 languageJavaC and C++C++C++
Server operating systemshostedLinux
Unix
Windows infousing Cygwin
LinuxLinuxLinux
macOS
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-free, schema definition possibleschema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateoptions to bring your own types, AVROyes infotext, numeric, histogramsyes 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 SQLnoSQL-like query language (Circonus Analytics Query Language: CAQL)SQL-like query languageno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
HTTP APIproprietary protocol using JSON
Supported programming languagesPython
R
Scala
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
.Net
C++
Java
PHP
Python
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infoCoprocessors in Javayes, in LuaJavaScriptno
Triggersyesnonono
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic, metric affinity per nodeShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
configurable replication factor, datacenter awareSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual ConsistencyImmediate consistency per node, eventual consistency across nodesEventual 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)noDocument is locked during a transaction
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.noyesnonoyes infousing specific database classes
User concepts infoAccess controlAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACnosimple password-based access controlno
More information provided by the system vendor
DatabricksHBaseIRONdbSequoiadbTkrzw 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
DatabricksHBaseIRONdbSequoiadbTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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