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DBMS > GigaSpaces vs. Graph Engine vs. Microsoft Azure Data Explorer vs. RDF4J vs. TimescaleDB

System Properties Comparison GigaSpaces vs. Graph Engine vs. Microsoft Azure Data Explorer vs. RDF4J vs. TimescaleDB

Editorial information provided by DB-Engines
NameGigaSpaces  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsA distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineFully managed big data interactive analytics platformRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelDocument store
Object oriented DBMS infoValues are user defined objects
Graph DBMS
Key-value store
Relational DBMS infocolumn orientedRDF storeTime Series DBMS
Secondary database modelsGraph DBMS
Search engine
Document 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
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.03
Rank#188  Overall
#32  Document stores
#6  Object oriented DBMS
Score0.67
Rank#232  Overall
#21  Graph DBMS
#34  Key-value stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.74
Rank#222  Overall
#9  RDF stores
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websitewww.gigaspaces.comwww.graphengine.ioazure.microsoft.com/­services/­data-explorerrdf4j.orgwww.timescale.com
Technical documentationdocs.gigaspaces.com/­latest/­landing.htmlwww.graphengine.io/­docs/­manualdocs.microsoft.com/­en-us/­azure/­data-explorerrdf4j.org/­documentationdocs.timescale.com
DeveloperGigaspaces TechnologiesMicrosoftMicrosoftSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Timescale
Initial release20002010201920042017
Current release15.5, September 2020cloud service with continuous releases2.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2; Commercial licenses availableOpen Source infoMIT LicensecommercialOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache 2.0
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 languageJava, C++, .Net.NET and CJavaC
Server operating systemsLinux
macOS
Solaris
Windows
.NEThostedLinux
OS X
Unix
Windows
Linux
OS X
Windows
Data schemeschema-freeyesFixed schema with schema-less datatypes (dynamic)yes infoRDF Schemasyes
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.no infoXML can be used for describing objects metadatanoyesyes
Secondary indexesyesall fields are automatically indexedyesyes
SQL infoSupport of SQLSQL-99 for query and DML statementsnoKusto Query Language (KQL), SQL subsetnoyes infofull PostgreSQL SQL syntax
APIs and other access methodsGigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
RESTful HTTP 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
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languages.Net
C++
Java
Python
Scala
C#
C++
F#
Visual Basic
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Java
PHP
Python
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyesyesYes, possible languages: KQL, Python, Ryesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyes, event driven architecturenoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesyes
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningSharding infoImplicit feature of the cloud servicenoneyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.noneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoMap-Reduce pattern can be built with XAP task executorsSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACID infoIsolation support depends on the API usedACID
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 storageyesyes infoin-memory storage is supported as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlRole-based access controlAzure Active Directory Authenticationnofine grained access rights according to SQL-standard

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More resources
GigaSpacesGraph Engine infoformer name: TrinityMicrosoft Azure Data ExplorerRDF4J infoformerly known as SesameTimescaleDB
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