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

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

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonFatDB  Xexclude from comparisonGoogle Cloud Datastore  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.FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksA .NET NoSQL DBMS that can integrate with and extend SQL Server.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformDistributed 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 store
Key-value store
Document storeEvent 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
Score0.19
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.ibm.com/­products/­db2-event-storewww.tigergraph.com
Technical documentationcloud.google.com/­datastore/­docswww.ibm.com/­docs/­en/­db2-event-storedocs.tigergraph.com
DeveloperAtos Convergence CreatorsFatCloudGoogleIBM
Initial release20162012200820172017
Current release17032.0
License infoCommercial or Open Sourcecommercialcommercialcommercialcommercial infofree developer edition availablecommercial
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC#C and C++C++
Server operating systemsLinuxWindowshostedLinux infoLinux, macOS, Windows for the developer additionLinux
Data schemeSchema and schema-less with LDAP viewsschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateoptionalyesyes, details hereyesyes
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 indexesyesyesyesno
SQL infoSupport of SQLnono infoVia inetgration in SQL ServerSQL-like query language (GQL)yes infothrough the embedded Spark runtimeSQL-like query language (GSQL)
APIs and other access methodsLDAP.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
Supported programming languagesAll languages with LDAP bindingsC#.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
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 proceduresnoyes infovia applicationsusing Google App Engineyesyes
Triggersyesyes infovia applicationsCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factorMulti-source replication using PaxosActive-active shard replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infousing Google Cloud Dataflownoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Immediate Consistency
Immediate 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 integritynonoyes infovia ReferenceProperties or Ancestor pathsnoyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsnoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesNo - written data is immutableyes
Durability infoSupport for making data persistentyesyesyesYes - 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.yesnoyesno
User concepts infoAccess controlLDAP bind authenticationno infoCan implement custom security layer via applicationsAccess 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 RepositoryFatDBGoogle Cloud DatastoreIBM Db2 Event StoreTigerGraph
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