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DBMS > FatDB vs. GeoSpock vs. Google Cloud Datastore vs. Teradata Aster vs. Warp 10

System Properties Comparison FatDB vs. GeoSpock vs. Google Cloud Datastore vs. Teradata Aster vs. Warp 10

Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonGeoSpock  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonTeradata Aster  Xexclude from comparisonWarp 10  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Spatial and temporal data processing engine for extreme data scaleAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformPlatform for big data analytics on multistructured data sources and typesTimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase
Primary database modelDocument store
Key-value store
Relational DBMSDocument storeRelational DBMSTime Series DBMS
Secondary database modelsTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score0.14
Rank#344  Overall
#32  Time Series DBMS
Websitegeospock.comcloud.google.com/­datastorewww.warp10.io
Technical documentationcloud.google.com/­datastore/­docswww.warp10.io/­content/­02_Getting_started
DeveloperFatCloudGeoSpockGoogleTeradataSenX
Initial release2012200820052015
Current release2.0, September 2019
License infoCommercial or Open SourcecommercialcommercialcommercialcommercialOpen Source infoApache License 2.0
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#Java, JavascriptJava
Server operating systemsWindowshostedhostedLinuxLinux
OS X
Windows
Data schemeschema-freeyesschema-freeFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Storeschema-free
Typing infopredefined data types such as float or dateyesyesyes, 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.nonoyes infoin Aster File Storeno
Secondary indexesyestemporal, categoricalyesyesno
SQL infoSupport of SQLno infoVia inetgration in SQL ServerANSI SQL for query only (using Presto)SQL-like query language (GQL)yesno
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
OLE DB
HTTP API
Jupyter
WebSocket
Supported programming languagesC#.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresyes infovia applicationsnousing Google App EngineR packagesyes infoWarpScript
Triggersyes infovia applicationsnoCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic shardingShardingShardingSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication using Paxosyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.selectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnoyes infousing Google Cloud Dataflowyes infoSQL Map-Reduce Frameworkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate 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 or Eventual Consistency depending on configurationImmediate Consistency infobased on HBase
Foreign keys infoReferential integritynonoyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononoyes
User concepts infoAccess controlno infoCan implement custom security layer via applicationsAccess rights for users can be defined per tableAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standardMandatory use of cryptographic tokens, containing fine-grained authorizations

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More resources
FatDBGeoSpockGoogle Cloud DatastoreTeradata AsterWarp 10
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