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DBMS > FatDB vs. Firebase Realtime Database vs. Pinecone vs. Spark SQL

System Properties Comparison FatDB vs. Firebase Realtime Database vs. Pinecone vs. Spark SQL

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonPinecone  Xexclude from comparisonSpark SQL  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.A managed, cloud-native vector databaseSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Key-value store
Document storeVector DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.64
Rank#39  Overall
#6  Document stores
Score3.23
Rank#92  Overall
#3  Vector DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitefirebase.google.com/­products/­realtime-databasewww.pinecone.iospark.apache.org/­sql
Technical documentationfirebase.google.com/­docs/­databasedocs.pinecone.io/­docs/­overviewspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFatCloudGoogle infoacquired by Google 2014Pinecone Systems, IncApache Software Foundation
Initial release2012201220192014
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC#Scala
Server operating systemsWindowshostedhostedLinux
OS X
Windows
Data schemeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesString, 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 indexesyesyesno
SQL infoSupport of SQLno infoVia inetgration in SQL ServernonoSQL-like DML and DDL statements
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
Android
iOS
JavaScript API
RESTful HTTP API
RESTful HTTP APIJDBC
ODBC
Supported programming languagesC#Java
JavaScript
Objective-C
PythonJava
Python
R
Scala
Server-side scripts infoStored proceduresyes infovia applicationslimited functionality with using 'rules'no
Triggersyes infovia applicationsCallbacks are triggered when data changesno
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Eventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesno
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.nono
User concepts infoAccess controlno infoCan implement custom security layer via applicationsyes, based on authentication and database rulesno

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More resources
FatDBFirebase Realtime DatabasePineconeSpark SQL
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