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DBMS > Databricks vs. HBase vs. Kinetica vs. VoltDB

System Properties Comparison Databricks vs. HBase vs. Kinetica vs. VoltDB

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHBase  Xexclude from comparisonKinetica  Xexclude from comparisonVoltDB  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 BigTableFully vectorized database across both GPUs and CPUsDistributed In-Memory NewSQL RDBMS infoUsed for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory
Primary database modelDocument store
Relational DBMS
Wide column storeRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series 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
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score1.44
Rank#158  Overall
#73  Relational DBMS
Websitewww.databricks.comhbase.apache.orgwww.kinetica.comwww.voltdb.com
Technical documentationdocs.databricks.comhbase.apache.org/­book.htmldocs.kinetica.comdocs.voltdb.com
DeveloperDatabricksApache Software Foundation infoApache top-level project, originally developed by PowersetKineticaVoltDB Inc.
Initial release2013200820122010
Current release2.3.4, January 20217.1, August 202111.3, April 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache version 2commercialOpen Source infoAGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++Java, C++
Server operating systemshostedLinux
Unix
Windows infousing Cygwin
LinuxLinux
OS X infofor development
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-free, schema definition possibleyesyes
Typing infopredefined data types such as float or dateoptions to bring your own types, AVROyesyes
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.yesnono
Secondary indexesyesnoyesyes
SQL infoSupport of SQLwith Databricks SQLnoSQL-like DML and DDL statementsyes infoonly a subset of SQL 99
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
JDBC
ODBC
RESTful HTTP API
Java API
JDBC
RESTful HTTP/JSON API
Supported programming languagesPython
R
Scala
C
C#
C++
Groovy
Java
PHP
Python
Scala
C++
Java
JavaScript (Node.js)
Python
C#
C++
Erlang infonot officially supported
Go
Java
JavaScript infoNode.js
PHP
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infoCoprocessors in Javauser defined functionsJava
Triggersyesyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
Source-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyesno infoFOREIGN KEY constraints are not supported
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDSingle row ACID (across millions of columns)noACID infoTransactions are executed single-threaded within stored procedures
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infoData access is serialized by the server
Durability infoSupport for making data persistentyesyesyesyes infoSnapshots and command logging
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 controlAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAccess rights for users and roles on table levelUsers and roles with access to stored procedures
More information provided by the system vendor
DatabricksHBaseKineticaVoltDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksHBaseKineticaVoltDB
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