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DBMS > Apache Hive vs. Databricks vs. EDB Postgres

System Properties Comparison Apache Hive vs. Databricks vs. EDB Postgres

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Editorial information provided by DB-Engines
NameApache Hive  Xexclude from comparisonDatabricks  Xexclude from comparisonEDB Postgres  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.The EDB Postgres Platform is an enterprise-class data management platform based on the open source database PostgreSQL with flexible deployment options and Oracle compatibility features, complemented by tool kits for management, integration, and migration.
Primary database modelRelational DBMSDocument store
Relational DBMS
Relational DBMS
Secondary database modelsDocument store
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
Score1.64
Rank#133  Overall
#61  Relational DBMS
Websitehive.apache.orgwww.databricks.comwww.enterprisedb.com
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homedocs.databricks.comwww.enterprisedb.com/­docs
DeveloperApache Software Foundation infoinitially developed by FacebookDatabricksEnterpriseDB
Initial release201220132005
Current release3.1.3, April 202214, December 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercial infoBSD for PostgreSQL-components
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC
Server operating systemsAll OS with a Java VMhostedLinux
Windows
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 infospecific XML-type available, but no XML query functionality.
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementswith Databricks SQLyes infostandard with numerous extensions
APIs and other access methodsJDBC
ODBC
Thrift
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC++
Java
PHP
Python
Python
R
Scala
.Net
C
C++
Delphi
Java
Perl
PHP
Python
Tcl
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.
Triggersnoyes
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning infoby hash, list or range
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesMulti-source 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
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
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.nono
User concepts infoAccess controlAccess rights for users, groups and rolesfine grained access rights according to SQL-standard

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Apache HiveDatabricksEDB Postgres
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