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DBMS > AnzoGraph DB vs. GigaSpaces vs. Hazelcast vs. Microsoft Azure Data Explorer vs. TerarkDB

System Properties Comparison AnzoGraph DB vs. GigaSpaces vs. Hazelcast vs. Microsoft Azure Data Explorer vs. TerarkDB

Editorial information provided by DB-Engines
NameAnzoGraph DB  Xexclude from comparisonGigaSpaces  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonTerarkDB  Xexclude from comparison
DescriptionScalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualizationHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsA widely adopted in-memory data gridFully managed big data interactive analytics platformA key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB
Primary database modelGraph DBMS
RDF store
Document store
Object oriented DBMS infoValues are user defined objects
Key-value storeRelational DBMS infocolumn orientedKey-value store
Secondary database modelsGraph DBMS
Search engine
Document 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.23
Rank#307  Overall
#24  Graph DBMS
#13  RDF stores
Score0.97
Rank#192  Overall
#32  Document stores
#6  Object oriented DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitecambridgesemantics.com/­anzographwww.gigaspaces.comhazelcast.comazure.microsoft.com/­services/­data-explorergithub.com/­bytedance/­terarkdb
Technical documentationdocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmdocs.gigaspaces.com/­latest/­landing.htmlhazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKc
DeveloperCambridge SemanticsGigaspaces TechnologiesHazelcastMicrosoftByteDance, originally Terark
Initial release20182000200820192016
Current release2.3, January 202115.5, September 20205.3.6, November 2023cloud service with continuous releases
License infoCommercial or Open Sourcecommercial infofree trial version availableOpen Source infoApache Version 2; Commercial licenses availableOpen Source infoApache Version 2; commercial licenses availablecommercialcommercial inforestricted open source version available
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .NetJavaC++
Server operating systemsLinuxLinux
macOS
Solaris
Windows
All OS with a Java VMhosted
Data schemeSchema-free and OWL/RDFS-schema supportschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)schema-free
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-typesno
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.nono infoXML can be used for describing objects metadatayes infothe object must implement a serialization strategyyesno
Secondary indexesnoyesyesall fields are automatically indexedno
SQL infoSupport of SQLSPARQL and SPARQL* as primary query language. Cypher preview.SQL-99 for query and DML statementsSQL-like query languageKusto Query Language (KQL), SQL subsetno
APIs and other access methodsApache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
JCache
JPA
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
C++ API
Java API
Supported programming languagesC++
Java
Python
.Net
C++
Java
Python
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++
Java
Server-side scripts infoStored proceduresuser defined functions and aggregatesyesyes infoEvent Listeners, Executor ServicesYes, possible languages: KQL, Python, Rno
Triggersnoyes, event driven architectureyes infoEventsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingShardingShardingSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication in MPP-ClusterMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
yes infoReplicated Mapyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsKerberos/HDFS data loadingyes infoMap-Reduce pattern can be built with XAP task executorsyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency in MPP-ClusterImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityno infonot needed in graphsnononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDone or two-phase-commit; repeatable reads; read commitednono
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.yesyesyesnoyes
User concepts infoAccess controlAccess rights for users and rolesRole-based access controlRole-based access controlAzure Active Directory Authenticationno

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More resources
AnzoGraph DBGigaSpacesHazelcastMicrosoft Azure Data ExplorerTerarkDB
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