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DBMS > AnzoGraph DB vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Microsoft Azure SQL Database vs. Trafodion

System Properties Comparison AnzoGraph DB vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Microsoft Azure SQL Database vs. Trafodion

Editorial information provided by DB-Engines
NameAnzoGraph DB  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonTrafodion  Xexclude from comparison
Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.
DescriptionScalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualizationA widely adopted in-memory data gridFully managed big data interactive analytics platformDatabase as a Service offering with high compatibility to Microsoft SQL ServerTransactional SQL-on-Hadoop DBMS
Primary database modelGraph DBMS
RDF store
Key-value storeRelational DBMS infocolumn orientedRelational DBMSRelational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12Document 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
Document store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.23
Rank#307  Overall
#24  Graph DBMS
#13  RDF stores
Score5.97
Rank#57  Overall
#6  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score77.99
Rank#16  Overall
#11  Relational DBMS
Websitecambridgesemantics.com/­anzographhazelcast.comazure.microsoft.com/­services/­data-explorerazure.microsoft.com/­en-us/­products/­azure-sql/­databasetrafodion.apache.org
Technical documentationdocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmhazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.microsoft.com/­en-us/­azure/­azure-sqltrafodion.apache.org/­documentation.html
DeveloperCambridge SemanticsHazelcastMicrosoftMicrosoftApache Software Foundation, originally developed by HP
Initial release20182008201920102014
Current release2.3, January 20215.3.6, November 2023cloud service with continuous releasesV122.3.0, February 2019
License infoCommercial or Open Sourcecommercial infofree trial version availableOpen Source infoApache Version 2; commercial licenses availablecommercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++C++, Java
Server operating systemsLinuxAll OS with a Java VMhostedhostedLinux
Data schemeSchema-free and OWL/RDFS-schema supportschema-freeFixed schema with schema-less datatypes (dynamic)yesyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyes
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.noyes infothe object must implement a serialization strategyyesyesno
Secondary indexesnoyesall fields are automatically indexedyesyes
SQL infoSupport of SQLSPARQL and SPARQL* as primary query language. Cypher preview.SQL-like query languageKusto Query Language (KQL), SQL subsetyesyes
APIs and other access methodsApache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
JCache
JPA
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
ODBC
ADO.NET
JDBC
ODBC
Supported programming languagesC++
Java
Python
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.Net
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infoEvent Listeners, Executor ServicesYes, possible languages: KQL, Python, RTransact SQLJava Stored Procedures
Triggersnoyes infoEventsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesno
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingShardingSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication in MPP-Clusteryes infoReplicated Mapyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes, with always 3 replicas availableyes, via HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsKerberos/HDFS data loadingyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoyes infovia user defined functions and HBase
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency in MPP-ClusterImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno infonot needed in graphsnonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDone or two-phase-commit; repeatable reads; read commitednoACIDACID
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.yesyesnono
User concepts infoAccess controlAccess rights for users and rolesRole-based access controlAzure Active Directory Authenticationfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
AnzoGraph DBHazelcastMicrosoft Azure Data ExplorerMicrosoft Azure SQL Database infoformerly SQL AzureTrafodion
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