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DBMS > Microsoft Azure Table Storage vs. openGemini vs. Spark SQL

System Properties Comparison Microsoft Azure Table Storage vs. openGemini vs. Spark SQL

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Editorial information provided by DB-Engines
NameMicrosoft Azure Table Storage  Xexclude from comparisonopenGemini  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionA Wide Column Store for rapid development using massive semi-structured datasetsAn open source distributed Time Series DBMS with high concurrency, high performance, and high scalabilitySpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelWide column storeTime Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.04
Rank#77  Overall
#6  Wide column stores
Score0.09
Rank#361  Overall
#37  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteazure.microsoft.com/­en-us/­services/­storage/­tableswww.opengemini.org
github.com/­openGemini
spark.apache.org/­sql
Technical documentationdocs.opengemini.org/­guidespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperMicrosoftHuawei and openGemini communityApache Software Foundation
Initial release201220222014
Current release1.1, July 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoScala
Server operating systemshostedLinux
Windows
Linux
OS X
Windows
Data schemeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesInteger, Float, Boolean, Stringyes
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.nonono
Secondary indexesnoyesno
SQL infoSupport of SQLnoSQL-like query languageSQL-like DML and DDL statements
APIs and other access methodsRESTful HTTP APIHTTP RESTJDBC
ODBC
Supported programming languages.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C++
Go
Java
JavaScript (Node.js)
Python
Rust
Java
Python
R
Scala
Server-side scripts infoStored proceduresnonono
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataoptimistic lockingnono
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.noyesno
User concepts infoAccess controlAccess rights based on private key authentication or shared access signaturesAdministrators and common users accountsno

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More resources
Microsoft Azure Table StorageopenGeminiSpark SQL
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