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DBMS > Atos Standard Common Repository vs. Blueflood vs. Firebase Realtime Database vs. IRONdb vs. Spark SQL

System Properties Comparison Atos Standard Common Repository vs. Blueflood vs. Firebase Realtime Database vs. IRONdb vs. Spark SQL

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonBlueflood  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonIRONdb  Xexclude from comparisonSpark SQL  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksScalable TimeSeries DBMS based on CassandraCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicitySpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Key-value store
Time Series DBMSDocument storeTime Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.06
Rank#353  Overall
#34  Time Series DBMS
Score14.29
Rank#39  Overall
#6  Document stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositoryblueflood.iofirebase.google.com/­products/­realtime-databasewww.circonus.com/solutions/time-series-database/spark.apache.org/­sql
Technical documentationgithub.com/­rax-maas/­blueflood/­wikifirebase.google.com/­docs/­databasedocs.circonus.com/irondb/category/getting-startedspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAtos Convergence CreatorsRackspaceGoogle infoacquired by Google 2014Circonus LLC.Apache Software Foundation
Initial release20162013201220172014
Current release1703V0.10.20, January 20183.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC and C++Scala
Server operating systemsLinuxLinux
OS X
hostedLinuxLinux
OS X
Windows
Data schemeSchema and schema-less with LDAP viewspredefined schemeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateoptionalyesyesyes infotext, numeric, histogramsyes
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.yesnononono
Secondary indexesyesnoyesnono
SQL infoSupport of SQLnononoSQL-like query language (Circonus Analytics Query Language: CAQL)SQL-like DML and DDL statements
APIs and other access methodsLDAPHTTP RESTAndroid
iOS
JavaScript API
RESTful HTTP API
HTTP APIJDBC
ODBC
Supported programming languagesAll languages with LDAP bindingsJava
JavaScript
Objective-C
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresnonolimited functionality with using 'rules'yes, in Luano
TriggersyesnoCallbacks are triggered when data changesnono
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionSharding infobased on CassandraAutomatic, metric affinity per nodeyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factor infobased on Cassandraconfigurable replication factor, datacenter awarenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Eventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsnoyesnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonono
User concepts infoAccess controlLDAP bind authenticationnoyes, based on authentication and database rulesnono

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More resources
Atos Standard Common RepositoryBluefloodFirebase Realtime DatabaseIRONdbSpark SQL
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