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

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

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Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonCoveo  Xexclude from comparisonSpark SQL  Xexclude from comparisonYaacomo  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 networksAI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code developmentSpark 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 computing
Primary database modelDocument store
Key-value store
Search engineRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.11
Rank#118  Overall
#11  Search engines
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.coveo.comspark.apache.org/­sqlyaacomo.com
Technical documentationdocs.coveo.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAtos Convergence CreatorsCoveoApache Software FoundationQ2WEB GmbH
Initial release2016201220142009
Current release17033.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaScala
Server operating systemsLinuxhostedLinux
OS X
Windows
Android
Linux
Windows
Data schemeSchema and schema-less with LDAP viewshybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configurationyesyes
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 indexesyesyesnoyes
SQL infoSupport of SQLnonoSQL-like DML and DDL statementsyes
APIs and other access methodsLDAPRESTful HTTP APIJDBC
ODBC
JDBC
ODBC
Supported programming languagesAll languages with LDAP bindingsC#
Java
JavaScript
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresnonono
Triggersyesyesnoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionyesyes, utilizing Spark Corehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesyesnoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsyesnoACID
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 authenticationgranular access controls, API key management, content filtersnofine grained access rights according to SQL-standard

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More resources
Atos Standard Common RepositoryCoveoSpark SQLYaacomo
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