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DBMS > Atos Standard Common Repository vs. Spark SQL vs. Yaacomo vs. YTsaurus

System Properties Comparison Atos Standard Common Repository vs. Spark SQL vs. Yaacomo vs. YTsaurus

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Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonSpark SQL  Xexclude from comparisonYaacomo  Xexclude from comparisonYTsaurus  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksSpark SQL is a component on top of 'Spark Core' for structured data processingOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computingYTsaurus is an open source platform for distributed storage and processing.
Primary database modelDocument store
Key-value store
Relational DBMSRelational DBMSDocument store
Key-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.21
Rank#324  Overall
#45  Document stores
#48  Key-value stores
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositoryspark.apache.org/­sqlyaacomo.comytsaurus.tech
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmlytsaurus.tech/­docs/­en
DeveloperAtos Convergence CreatorsApache Software FoundationQ2WEB GmbHYandex
Initial release2016201420092023
Current release17033.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen Source infoApache License, Version 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScalaC++
Server operating systemsLinuxLinux
OS X
Windows
Android
Linux
Windows
Ubuntu
Data schemeSchema and schema-less with LDAP viewsyesyes
Typing infopredefined data types such as float or dateoptionalyesyesyes
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.yesnonono
Secondary indexesyesnoyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsyesYQL, an SQL-based language, is supported
APIs and other access methodsLDAPJDBC
ODBC
JDBC
ODBC
Supported programming languagesAll languages with LDAP bindingsJava
Python
R
Scala
C++
Go
Java
JavaScript
Python
Server-side scripts infoStored proceduresnono
Triggersyesnoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionyes, utilizing Spark Corehorizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesnoneSource-replica replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsnoACIDACID
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.yesnoyes
User concepts infoAccess controlLDAP bind authenticationnofine grained access rights according to SQL-standardAccess Control Lists

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More resources
Atos Standard Common RepositorySpark SQLYaacomoYTsaurus
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