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DBMS > Apache Spark (SQL) vs. Firebase Realtime Database

System Properties Comparison Apache Spark (SQL) vs. Firebase Realtime Database

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparison
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.
Primary database modelRelational DBMSDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score17.39
Rank#32  Overall
#20  Relational DBMS
Score13.06
Rank#39  Overall
#6  Document stores
Websitespark.apache.org/­sqlfirebase.google.com/­products/­realtime-database
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmlfirebase.google.com/­docs/­database
DeveloperApache Software FoundationGoogle infoacquired by Google 2014
Initial release20142012
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScala
Server operating systemsLinux
OS X
Windows
hosted
Data schemeyesschema-free
Typing infopredefined data types such as float or dateyesyes
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.nono
Secondary indexesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
Android
iOS
JavaScript API
RESTful HTTP API
Supported programming languagesJava
Python
R
Scala
Java
JavaScript
Objective-C
Server-side scripts infoStored proceduresnolimited functionality with using 'rules'
TriggersnoCallbacks are triggered when data changes
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyes
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlnoyes, based on authentication and database rules

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More resources
Apache Spark (SQL)Firebase Realtime Database
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