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DBMS > Atos Standard Common Repository vs. Google Cloud Datastore vs. InterSystems Caché vs. Riak TS

System Properties Comparison Atos Standard Common Repository vs. Google Cloud Datastore vs. InterSystems Caché vs. Riak TS

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Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonInterSystems Caché  Xexclude from comparisonRiak TS  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA multi-model DBMS and application serverRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KV
Primary database modelDocument store
Key-value store
Document storeKey-value store
Object oriented DBMS
Relational DBMS
Time Series DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score0.28
Rank#307  Overall
#27  Time Series DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorycloud.google.com/­datastorewww.intersystems.com/­products/­cache
Technical documentationcloud.google.com/­datastore/­docsdocs.intersystems.comwww.tiot.jp/­riak-docs/­riak/­ts/­latest
DeveloperAtos Convergence CreatorsGoogleInterSystemsOpen Source, formerly Basho Technologies
Initial release2016200819972015
Current release17032018.1.4, May 20203.0.0, September 2022
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source
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 languageJavaErlang
Server operating systemsLinuxhostedAIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows
Linux
OS X
Data schemeSchema and schema-less with LDAP viewsschema-freedepending on used data modelschema-free
Typing infopredefined data types such as float or dateoptionalyes, details hereyesno
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.yesnoyesno
Secondary indexesyesyesyesrestricted
SQL infoSupport of SQLnoSQL-like query language (GQL)yesyes, limited
APIs and other access methodsLDAPgRPC (using protocol buffers) API
RESTful HTTP/JSON API
.NET Client API
JDBC
ODBC
RESTful HTTP API
HTTP API
Native Erlang Interface
Supported programming languagesAll languages with LDAP bindings.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Java
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresnousing Google App EngineyesErlang
TriggersyesCallbacks using the Google Apps Engineyesyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using PaxosSource-replica replicationselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsyesno infolinks between datasets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDno
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 authenticationAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and rolesno

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More resources
Atos Standard Common RepositoryGoogle Cloud DatastoreInterSystems CachéRiak TS
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