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DBMS > AnzoGraph DB vs. Hazelcast vs. IBM Db2 Event Store vs. Microsoft Azure Data Explorer vs. TerarkDB

System Properties Comparison AnzoGraph DB vs. Hazelcast vs. IBM Db2 Event Store vs. Microsoft Azure Data Explorer vs. TerarkDB

Editorial information provided by DB-Engines
NameAnzoGraph DB  Xexclude from comparisonHazelcast  Xexclude from comparisonIBM Db2 Event Store  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 virtualizationA widely adopted in-memory data gridDistributed Event Store optimized for Internet of Things use casesFully 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
Key-value storeEvent Store
Time Series DBMS
Relational DBMS infocolumn orientedKey-value store
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
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
Score0.19
Rank#323  Overall
#2  Event Stores
#28  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitecambridgesemantics.com/­anzographhazelcast.comwww.ibm.com/­products/­db2-event-storeazure.microsoft.com/­services/­data-explorergithub.com/­bytedance/­terarkdb
Technical documentationdocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmhazelcast.org/­imdg/­docswww.ibm.com/­docs/­en/­db2-event-storedocs.microsoft.com/­en-us/­azure/­data-explorerbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKc
DeveloperCambridge SemanticsHazelcastIBMMicrosoftByteDance, originally Terark
Initial release20182008201720192016
Current release2.3, January 20215.3.6, November 20232.0cloud service with continuous releases
License infoCommercial or Open Sourcecommercial infofree trial version availableOpen Source infoApache Version 2; commercial licenses availablecommercial infofree developer edition 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 languageJavaC and C++C++
Server operating systemsLinuxAll OS with a Java VMLinux infoLinux, macOS, Windows for the developer additionhosted
Data schemeSchema-free and OWL/RDFS-schema supportschema-freeyesFixed 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.noyes infothe object must implement a serialization strategynoyesno
Secondary indexesnoyesnoall fields are automatically indexedno
SQL infoSupport of SQLSPARQL and SPARQL* as primary query language. Cypher preview.SQL-like query languageyes infothrough the embedded Spark runtimeKusto Query Language (KQL), SQL subsetno
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
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
C++ API
Java API
Supported programming languagesC++
Java
Python
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++
Java
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infoEvent Listeners, Executor ServicesyesYes, possible languages: KQL, Python, Rno
Triggersnoyes infoEventsnoyes 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-Clusteryes infoReplicated MapActive-active shard replicationyes 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 loadingyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency in MPP-ClusterImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityno infonot needed in graphsnononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDone or two-phase-commit; repeatable reads; read commitednonono
Concurrency infoSupport for concurrent manipulation of datayesyesNo - written data is immutableyesyes
Durability infoSupport for making data persistentyesyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyesyes
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 controlfine grained access rights according to SQL-standardAzure Active Directory Authenticationno

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More resources
AnzoGraph DBHazelcastIBM Db2 Event StoreMicrosoft Azure Data ExplorerTerarkDB
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