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DBMS > Greenplum vs. HBase vs. Kinetica vs. Microsoft Azure SQL Database vs. Spark SQL

System Properties Comparison Greenplum vs. HBase vs. Kinetica vs. Microsoft Azure SQL Database vs. Spark SQL

Editorial information provided by DB-Engines
NameGreenplum  Xexclude from comparisonHBase  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAnalytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database infoA logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.Wide-column store based on Apache Hadoop and on concepts of BigTableFully vectorized database across both GPUs and CPUsDatabase as a Service offering with high compatibility to Microsoft SQL ServerSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSWide column storeRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Spatial DBMS
Time Series DBMS
Document store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score8.37
Rank#48  Overall
#30  Relational DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score77.99
Rank#16  Overall
#11  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitegreenplum.orghbase.apache.orgwww.kinetica.comazure.microsoft.com/­en-us/­products/­azure-sql/­databasespark.apache.org/­sql
Technical documentationdocs.greenplum.orghbase.apache.org/­book.htmldocs.kinetica.comdocs.microsoft.com/­en-us/­azure/­azure-sqlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperPivotal Software Inc.Apache Software Foundation infoApache top-level project, originally developed by PowersetKineticaMicrosoftApache Software Foundation
Initial release20052008201220102014
Current release7.0.0, September 20232.3.4, January 20217.1, August 2021V123.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache version 2commercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC, C++C++Scala
Server operating systemsLinuxLinux
Unix
Windows infousing Cygwin
LinuxhostedLinux
OS X
Windows
Data schemeyesschema-free, schema definition possibleyesyesyes
Typing infopredefined data types such as float or dateyesoptions to bring your own types, AVROyesyesyes
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.yes infosince Version 4.2nonoyesno
Secondary indexesyesnoyesyesno
SQL infoSupport of SQLyesnoSQL-like DML and DDL statementsyesSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
Java API
RESTful HTTP API
Thrift
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
JDBC
ODBC
Supported programming languagesC
Java
Perl
Python
R
C
C#
C++
Groovy
Java
PHP
Python
Scala
C++
Java
JavaScript (Node.js)
Python
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesyes infoCoprocessors in Javauser defined functionsTransact SQLno
Triggersyesyesyes infotriggers when inserted values for one or more columns fall within a specified rangeyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication
Source-replica replication
Source-replica replicationyes, with always 3 replicas availablenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyesnoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDSingle row ACID (across millions of columns)noACIDno
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.noyesyes infoGPU vRAM or System RAMno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAccess rights for users and roles on table levelfine grained access rights according to SQL-standardno

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More resources
GreenplumHBaseKineticaMicrosoft Azure SQL Database infoformerly SQL AzureSpark SQL
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