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DBMS > Apache Hive vs. Databricks vs. Microsoft Azure SQL Database

System Properties Comparison Apache Hive vs. Databricks vs. Microsoft Azure SQL Database

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Editorial information provided by DB-Engines
NameApache Hive  Xexclude from comparisonDatabricks  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  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.Database as a Service offering with high compatibility to Microsoft SQL Server
Primary database modelRelational DBMSDocument store
Relational DBMS
Relational DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score72.65
Rank#18  Overall
#12  Relational DBMS
Score99.29
Rank#12  Overall
#2  Document stores
#8  Relational DBMS
Score75.37
Rank#16  Overall
#11  Relational DBMS
Websitehive.apache.orgwww.databricks.comazure.microsoft.com/­en-us/­products/­azure-sql/­database
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homedocs.databricks.comdocs.microsoft.com/­en-us/­azure/­azure-sql
DeveloperApache Software Foundation infoinitially developed by FacebookDatabricksMicrosoft
Initial release201220132010
Current release3.1.3, April 2022V12
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++
Server operating systemsAll OS with a Java VMhostedhosted
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.yesyes
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementswith Databricks SQLyes
APIs and other access methodsJDBC
ODBC
Thrift
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
Supported programming languagesC++
Java
PHP
Python
Python
R
Scala
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesTransact SQL
Triggersnoyes
Partitioning methods infoMethods for storing different data on different nodesSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesyes, with always 3 replicas available
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
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
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.no
User concepts infoAccess controlAccess rights for users, groups and rolesfine grained access rights according to SQL-standard

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More resources
Apache HiveDatabricksMicrosoft Azure SQL Database infoformerly SQL Azure
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