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DBMS > Atos Standard Common Repository vs. Google Cloud Datastore vs. Graph Engine vs. IBM Db2 Event Store vs. TigerGraph

System Properties Comparison Atos Standard Common Repository vs. Google Cloud Datastore vs. Graph Engine vs. IBM Db2 Event Store vs. TigerGraph

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonIBM Db2 Event Store  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 PlatformA distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineDistributed Event Store optimized for Internet of Things use casesA complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time
Primary database modelDocument store
Key-value store
Document storeGraph DBMS
Key-value store
Event Store
Time Series DBMS
Graph DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score0.61
Rank#240  Overall
#21  Graph DBMS
#35  Key-value stores
Score0.19
Rank#323  Overall
#2  Event Stores
#28  Time Series DBMS
Score1.83
Rank#139  Overall
#13  Graph DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorycloud.google.com/­datastorewww.graphengine.iowww.ibm.com/­products/­db2-event-storewww.tigergraph.com
Technical documentationcloud.google.com/­datastore/­docswww.graphengine.io/­docs/­manualwww.ibm.com/­docs/­en/­db2-event-storedocs.tigergraph.com
DeveloperAtos Convergence CreatorsGoogleMicrosoftIBM
Initial release20162008201020172017
Current release17032.0
License infoCommercial or Open SourcecommercialcommercialOpen Source infoMIT Licensecommercial infofree developer edition availablecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava.NET and CC and C++C++
Server operating systemsLinuxhosted.NETLinux infoLinux, macOS, Windows for the developer additionLinux
Data schemeSchema and schema-less with LDAP viewsschema-freeyesyesyes
Typing infopredefined data types such as float or dateoptionalyes, details hereyesyesyes
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.yesnononono
Secondary indexesyesyesno
SQL infoSupport of SQLnoSQL-like query language (GQL)noyes infothrough the embedded Spark runtimeSQL-like query language (GSQL)
APIs and other access methodsLDAPgRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP APIADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
Supported programming languagesAll languages with LDAP bindings.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
F#
Visual Basic
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
C++
Java
Server-side scripts infoStored proceduresnousing Google App Engineyesyesyes
TriggersyesCallbacks using the Google Apps Enginenonono
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardinghorizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using PaxosActive-active shard replication
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.Eventual 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 TransactionsnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesNo - written data is immutableyes
Durability infoSupport for making data persistentyesyesoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesyesno
User concepts infoAccess controlLDAP bind authenticationAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standardRole-based access control

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More resources
Atos Standard Common RepositoryGoogle Cloud DatastoreGraph Engine infoformer name: TrinityIBM Db2 Event StoreTigerGraph
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