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

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

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Editorial information provided by DB-Engines
NameMicrosoft Azure Table Storage  Xexclude from comparisonPinecone  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionA Wide Column Store for rapid development using massive semi-structured datasetsA managed, cloud-native vector databaseSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelWide column storeVector DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.04
Rank#77  Overall
#6  Wide column stores
Score3.23
Rank#92  Overall
#2  Vector DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteazure.microsoft.com/­en-us/­services/­storage/­tableswww.pinecone.iospark.apache.org/­sql
Technical documentationdocs.pinecone.io/­docs/­overviewspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperMicrosoftPinecone Systems, IncApache Software Foundation
Initial release201220192014
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScala
Server operating systemshostedhostedLinux
OS X
Windows
Data schemeschema-freeyes
Typing infopredefined data types such as float or dateyesString, Number, Booleanyes
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 indexesnono
SQL infoSupport of SQLnonoSQL-like DML and DDL statements
APIs and other access methodsRESTful HTTP APIRESTful HTTP APIJDBC
ODBC
Supported programming languages.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
PythonJava
Python
R
Scala
Server-side scripts infoStored proceduresnono
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceyes, 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.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataoptimistic lockingno
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.nonono
User concepts infoAccess controlAccess rights based on private key authentication or shared access signaturesno

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More resources
Microsoft Azure Table StoragePineconeSpark SQL
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