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DBMS > Hazelcast vs. Microsoft Azure Data Explorer vs. Quasardb vs. Tarantool vs. Yanza

System Properties Comparison Hazelcast vs. Microsoft Azure Data Explorer vs. Quasardb vs. Tarantool vs. Yanza

Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonQuasardb  Xexclude from comparisonTarantool  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionA widely adopted in-memory data gridFully managed big data interactive analytics platformDistributed, high-performance timeseries databaseIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsTime Series DBMS for IoT Applications
Primary database modelKey-value storeRelational DBMS infocolumn orientedTime Series DBMSDocument store
Key-value store
Relational DBMS
Time Series 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
Spatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.97
Rank#57  Overall
#6  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.14
Rank#332  Overall
#29  Time Series DBMS
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websitehazelcast.comazure.microsoft.com/­services/­data-explorerquasar.aiwww.tarantool.ioyanza.com
Technical documentationhazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerdoc.quasar.ai/­masterwww.tarantool.io/­en/­doc
DeveloperHazelcastMicrosoftquasardbVKYanza
Initial release20082019200920082015
Current release5.3.6, November 2023cloud service with continuous releases3.14.1, January 20242.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availablecommercialcommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licensesOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial infofree version available
Cloud-based only infoOnly available as a cloud servicenoyesnonono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++C and C++
Server operating systemsAll OS with a Java VMhostedBSD
Linux
OS X
Windows
BSD
Linux
macOS
Windows
Data schemeschema-freeFixed schema with schema-less datatypes (dynamic)schema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsschema-free
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-typesyes infointeger and binarystring, double, decimal, uuid, integer, blob, boolean, datetimeno
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.yes infothe object must implement a serialization strategyyesnonono
Secondary indexesyesall fields are automatically indexedyes infowith tagsyesno
SQL infoSupport of SQLSQL-like query languageKusto Query Language (KQL), SQL subsetSQL-like query languageFull-featured ANSI SQL supportno
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP APIOpen binary protocolHTTP API
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
any language that supports HTTP calls
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor ServicesYes, possible languages: KQL, Python, RnoLua, C and SQL stored proceduresno
Triggersyes infoEventsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyes, before/after data modification events, on replication events, client session eventsyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud serviceSharding infoconsistent hashingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.none
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated Mapyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication with selectable replication factorAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkwith Hadoop integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Immediate ConsistencyCasual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Immediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitednoACIDACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyesyesyes infoby using LevelDByes, write ahead loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes infoTransient modeyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlRole-based access controlAzure Active Directory AuthenticationCryptographically strong user authentication and audit trailAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles
no

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More resources
HazelcastMicrosoft Azure Data ExplorerQuasardbTarantoolYanza
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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