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DBMS > Atos Standard Common Repository vs. ToroDB vs. Vertica

System Properties Comparison Atos Standard Common Repository vs. ToroDB vs. Vertica

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Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonToroDB  Xexclude from comparisonVertica infoVertica Analytics Platform  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksA MongoDB-compatible JSON document store, built on top of PostgreSQLCloud or on-premises analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelDocument store
Key-value store
Document storeRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.16
Rank#344  Overall
#49  Document stores
Score18.51
Rank#38  Overall
#23  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.torodb.comwww.vertica.com
Technical documentationwww.torodb.com/­stampede/­docsvertica.com/­documentation
DeveloperAtos Convergence Creators8KdataMicro Focus infoprior to that Hewlett Packard
Initial release201620162005
Current release170311.1, February 2022
License infoCommercial or Open SourcecommercialOpen Source infoAGPL-V3commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Vertica Accelerator’s high-performance analytics and machine learning with SQL or Python is available as a managed service.
Implementation languageJavaJavaC++
Server operating systemsLinuxAll OS with a Java 7 VMLinux
Data schemeSchema and schema-less with LDAP viewsschema-freeyes, but unstructured data can be stored in specific Flex-Tables
Typing infopredefined data types such as float or dateoptionalyes infostring, integer, double, boolean, date, object_idyes: BINARY, BOOLEAN, CHAR, VARCHAR, LONG VARCHAR, DATE, TIME, TIMESTAMP, INTERVAL, INTERVAL DAY TO SECOND, INTERVAL YEAR TO MONTH, DOUBLE PRECISION, FLOAT, INTEGER, BIGINT, SMALLINT,NUMERIC, DECIMAL, NUMBER, MONEY,GEOMETRY
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.yesnono
Secondary indexesyesNo Indexes Required
SQL infoSupport of SQLnoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsLDAPADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesAll languages with LDAP bindingsC#
C++
Java
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyesnoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replicationMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlLDAP bind authenticationAccess rights for users and rolesfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Atos Standard Common RepositoryToroDBVertica infoVertica Analytics Platform
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy on-premises,...
» more
Competitive advantagesFast, scalable and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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Atos Standard Common RepositoryToroDBVertica infoVertica Analytics Platform
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