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

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

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Editorial information provided by DB-Engines
NameMicrosoft Azure Table Storage  Xexclude from comparisonPinecone  Xexclude from comparisonSpark SQL  Xexclude from comparisonSTSdb  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 processingKey-Value Store with special method for indexing infooptimized for high performance using a special indexing method
Primary database modelWide column storeVector DBMSRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.48
Rank#75  Overall
#6  Wide column stores
Score3.16
Rank#95  Overall
#2  Vector DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.04
Rank#360  Overall
#52  Key-value stores
Websiteazure.microsoft.com/­en-us/­services/­storage/­tableswww.pinecone.iospark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4
Technical documentationdocs.pinecone.io/­docs/­overviewspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperMicrosoftPinecone Systems, IncApache Software FoundationSTS Soft SC
Initial release2012201920142011
Current release3.5.0 ( 2.13), September 20234.0.8, September 2015
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0Open Source infoGPLv2, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC#
Server operating systemshostedhostedLinux
OS X
Windows
Windows
Data schemeschema-freeyesyes
Typing infopredefined data types such as float or dateyesString, Number, Booleanyesyes infoprimitive types and user defined types (classes)
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 indexesnonono
SQL infoSupport of SQLnonoSQL-like DML and DDL statementsno
APIs and other access methodsRESTful HTTP APIRESTful HTTP APIJDBC
ODBC
.NET Client API
Supported programming languages.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
PythonJava
Python
R
Scala
C#
Java
Server-side scripts infoStored proceduresnonono
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.nonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
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 datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
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 signaturesnono

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More resources
Microsoft Azure Table StoragePineconeSpark SQLSTSdb
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