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DBMS > Firebase Realtime Database vs. Spark SQL

System Properties Comparison Firebase Realtime Database vs. Spark SQL

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Editorial information provided by DB-Engines
NameFirebase Realtime Database  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score11.00
Rank#41  Overall
#7  Document stores
Score17.19
Rank#35  Overall
#21  Relational DBMS
Websitefirebase.google.com/­products/­realtime-databasespark.apache.org/­sql
Technical documentationfirebase.google.com/­docs/­databasespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperGoogle infoacquired by Google 2014Apache Software Foundation
Initial release20122014
Current releasev2.4.0, November 2018
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageScala
Server operating systemshostedLinux
OS X
Windows
Data schemeschema-freeyes
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 indexesyesno
SQL infoSupport of SQLnoSQL-like DML and DDL statements
APIs and other access methodsAndroid
iOS
JavaScript API
RESTful HTTP API
JDBC
ODBC
Supported programming languagesJava
JavaScript
Objective-C
Java
Python
R
Scala
Server-side scripts infoStored procedureslimited functionality with using 'rules'no
TriggersCallbacks are triggered when data changesno
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 datayesno
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 controlyes, based on authentication and database rulesno

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More resources
Firebase Realtime DatabaseSpark SQL
Conferences and events

Spark + AI Summit
San Francisco, California, 23-25 April 2019

Recent citations in the news

Realtime vs Cloud Firestore: Which Firebase Database to go?
12 October 2018, Appinventiv

How unsecured Firebase databases put critical data at risk
16 January 2019, TechTarget

On the verge of Next '19, Google must double down on cloud applications
1 April 2019, SiliconANGLE News

Google’s Cloud Firestore NoSQL database hits general availability
31 January 2019, TechCrunch

Firebase for Startups: A Must-Have or Non-Essential
23 January 2019, Appinventiv

provided by Google News

Delta Lake gives Apache Spark data sets new powers
24 April 2019, InfoWorld

Top 10 Books For Learning Apache Spark
29 March 2019, Analytics India Magazine

Apache Spark Is Great, But It's Not Perfect
3 April 2019, Datanami

Using Spark and Jenkins to Deploy Code into Hadoop Clusters
26 March 2019, DevOps.com

How Databricks Keeps Data Quality High with Delta
8 April 2019, Datanami

provided by Google News

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