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DBMS > Apache Hive vs. Databricks vs. Kinetica

System Properties Comparison Apache Hive vs. Databricks vs. Kinetica

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Editorial information provided by DB-Engines
NameApache Hive  Xexclude from comparisonDatabricks  Xexclude from comparisonKinetica  Xexclude from comparison
Descriptiondata warehouse software for querying and managing large distributed datasets, built on HadoopThe 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.Fully vectorized database across both GPUs and CPUs
Primary database modelRelational DBMSDocument store
Relational DBMS
Relational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score62.49
Rank#18  Overall
#12  Relational DBMS
Score90.03
Rank#13  Overall
#2  Document stores
#9  Relational DBMS
Score0.39
Rank#260  Overall
#120  Relational DBMS
Websitehive.apache.orgwww.databricks.comwww.kinetica.com
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homedocs.databricks.comdocs.kinetica.com
DeveloperApache Software Foundation infoinitially developed by FacebookDatabricksKinetica
Initial release201220132012
Current release3.1.3, April 20227.1, August 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++
Server operating systemsAll OS with a Java VMhostedLinux
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yes
Typing infopredefined data types such as float or dateyesyes
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.yesno
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementswith Databricks SQLSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
Thrift
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC++
Java
PHP
Python
Python
R
Scala
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesuser defined functions
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoGPU vRAM or System RAM
User concepts infoAccess controlAccess rights for users, groups and rolesAccess rights for users and roles on table level
More information provided by the system vendor
Apache HiveDatabricksKinetica
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Apache HiveDatabricksKinetica
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