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DBMS > Atos Standard Common Repository vs. Google Cloud Datastore vs. Memcached vs. Microsoft Azure Table Storage vs. TigerGraph

System Properties Comparison Atos Standard Common Repository vs. Google Cloud Datastore vs. Memcached vs. Microsoft Azure Table Storage vs. TigerGraph

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonMemcached  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonTigerGraph  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 networksAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformIn-memory key-value store, originally intended for cachingA Wide Column Store for rapid development using massive semi-structured datasetsA complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time
Primary database modelDocument store
Key-value store
Document storeKey-value storeWide column storeGraph DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score19.42
Rank#32  Overall
#4  Key-value stores
Score4.48
Rank#75  Overall
#6  Wide column stores
Score1.83
Rank#139  Overall
#13  Graph DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorycloud.google.com/­datastorewww.memcached.orgazure.microsoft.com/­en-us/­services/­storage/­tableswww.tigergraph.com
Technical documentationcloud.google.com/­datastore/­docsgithub.com/­memcached/­memcached/­wikidocs.tigergraph.com
DeveloperAtos Convergence CreatorsGoogleDanga Interactive infooriginally developed by Brad Fitzpatrick for LiveJournalMicrosoft
Initial release20162008200320122017
Current release17031.6.25, March 2024
License infoCommercial or Open SourcecommercialcommercialOpen Source infoBSD licensecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaCC++
Server operating systemsLinuxhostedFreeBSD
Linux
OS X
Unix
Windows
hostedLinux
Data schemeSchema and schema-less with LDAP viewsschema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateoptionalyes, details herenoyesyes
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 indexesyesyesnono
SQL infoSupport of SQLnoSQL-like query language (GQL)nonoSQL-like query language (GSQL)
APIs and other access methodsLDAPgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Proprietary protocolRESTful HTTP APIGSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
Supported programming languagesAll languages with LDAP bindings.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
Server-side scripts infoStored proceduresnousing Google App Enginenonoyes
TriggersyesCallbacks using the Google Apps Enginenonono
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardingnoneSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using Paxosnone infoRepcached, a Memcached patch, provides this functionallityyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownonoyes
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 Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnonoyes infoRelationships in graphs
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 Transactionsnooptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesnoyesyes
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 authenticationAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes infousing SASL (Simple Authentication and Security Layer) protocolAccess rights based on private key authentication or shared access signaturesRole-based access control

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More resources
Atos Standard Common RepositoryGoogle Cloud DatastoreMemcachedMicrosoft Azure Table StorageTigerGraph
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