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DBMS > Google Cloud Datastore vs. Graph Engine vs. IRONdb vs. Microsoft Azure Data Explorer vs. RDF4J

System Properties Comparison Google Cloud Datastore vs. Graph Engine vs. IRONdb vs. Microsoft Azure Data Explorer vs. RDF4J

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonIRONdb  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionAutomatically 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 engineA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityFully managed big data interactive analytics platformRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelDocument storeGraph DBMS
Key-value store
Time Series DBMSRelational DBMS infocolumn orientedRDF store
Secondary database modelsDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
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
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.69
Rank#230  Overall
#9  RDF stores
Websitecloud.google.com/­datastorewww.graphengine.iowww.circonus.com/solutions/time-series-database/azure.microsoft.com/­services/­data-explorerrdf4j.org
Technical documentationcloud.google.com/­datastore/­docswww.graphengine.io/­docs/­manualdocs.circonus.com/irondb/category/getting-starteddocs.microsoft.com/­en-us/­azure/­data-explorerrdf4j.org/­documentation
DeveloperGoogleMicrosoftCirconus LLC.MicrosoftSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release20082010201720192004
Current releaseV0.10.20, January 2018cloud service with continuous releases
License infoCommercial or Open SourcecommercialOpen Source infoMIT LicensecommercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud serviceyesnonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation language.NET and CC and C++Java
Server operating systemshosted.NETLinuxhostedLinux
OS X
Unix
Windows
Data schemeschema-freeyesschema-freeFixed schema with schema-less datatypes (dynamic)yes infoRDF Schemas
Typing infopredefined data types such as float or dateyes, details hereyesyes infotext, numeric, histogramsyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.nononoyes
Secondary indexesyesnoall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query language (GQL)noSQL-like query language (Circonus Analytics Query Language: CAQL)Kusto Query Language (KQL), SQL subsetno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP APIHTTP APIMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
F#
Visual Basic
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Java
PHP
Python
Server-side scripts infoStored proceduresusing Google App Engineyesyes, in LuaYes, possible languages: KQL, Python, Ryes
TriggersCallbacks using the Google Apps Enginenonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningAutomatic, metric affinity per nodeSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosconfigurable replication factor, datacenter awareyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud DataflownoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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 per node, eventual consistency across nodesEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnononoACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyesyesyes infoin-memory storage is supported as well
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnono
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noAzure Active Directory Authenticationno

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More resources
Google Cloud DatastoreGraph Engine infoformer name: TrinityIRONdbMicrosoft Azure Data ExplorerRDF4J infoformerly known as Sesame
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