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DBMS > Google Cloud Datastore vs. Manticore Search vs. MarkLogic vs. Microsoft Azure Data Explorer vs. RDF4J

System Properties Comparison Google Cloud Datastore vs. Manticore Search vs. MarkLogic vs. Microsoft Azure Data Explorer vs. RDF4J

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonManticore Search  Xexclude from comparisonMarkLogic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformMulti-storage database for search, including full-text search.Operational and transactional Enterprise NoSQL databaseFully 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 storeSearch engineDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS infocolumn orientedRDF store
Secondary database modelsTime Series DBMS infousing the Manticore Columnar LibraryDocument 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
Score4.49
Rank#79  Overall
#12  Document stores
Score0.23
Rank#317  Overall
#21  Search engines
Score6.50
Rank#56  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#5  Search engines
Score5.16
Rank#69  Overall
#37  Relational DBMS
Score0.71
Rank#231  Overall
#9  RDF stores
Websitecloud.google.com/­datastoremanticoresearch.comwww.marklogic.comazure.microsoft.com/­services/­data-explorerrdf4j.org
Technical documentationcloud.google.com/­datastore/­docsmanual.manticoresearch.comdocs.marklogic.comdocs.microsoft.com/­en-us/­azure/­data-explorerrdf4j.org/­documentation
DeveloperGoogleManticore SoftwareMarkLogic Corp.MicrosoftSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release20082017200120192004
Current release6.0, February 202311.0, December 2022cloud service with continuous releases
License infoCommercial or Open SourcecommercialOpen Source infoGPL version 2commercial inforestricted free version is availablecommercialOpen 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 languageC++C++Java
Server operating systemshostedFreeBSD
Linux
macOS
Windows
Linux
OS X
Windows
hostedLinux
OS X
Unix
Windows
Data schemeschema-freeFixed schemaschema-free infoSchema can be enforcedFixed schema with schema-less datatypes (dynamic)yes infoRDF Schemas
Typing infopredefined data types such as float or dateyes, details hereInt, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Booleanyesyes 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.noCan index from XMLyesyes
Secondary indexesyesyes infofull-text index on all search fieldsyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query language (GQL)SQL-like query languageyes infoSQL92Kusto Query Language (KQL), SQL subsetno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Microsoft 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
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Java
PHP
Python
Server-side scripts infoStored proceduresusing Google App Engineuser defined functionsyes infovia XQuery or JavaScriptYes, possible languages: KQL, Python, Ryes
TriggersCallbacks using the Google Apps Enginenoyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedShardingSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosSynchronous replication based on Galera libraryyesyes 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 Dataflownoyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsSpark 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 ConsistencyEventual 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 Transactionsyes infoisolated transactions for atomic changes and binary logging for safe writesACID infocan act as a resource manager in an XA/JTA transactionnoACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes infoThe original contents of fields are not stored in the Manticore index.yesyesyes 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.noyes, with Range Indexesno
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noRole-based access control at the document and subdocument levelsAzure Active Directory Authenticationno

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More resources
Google Cloud DatastoreManticore SearchMarkLogicMicrosoft Azure Data ExplorerRDF4J infoformerly known as Sesame
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