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DBMS > Datastax Enterprise vs. Hazelcast vs. Heroic vs. Microsoft Azure Data Explorer vs. Microsoft Azure Table Storage

System Properties Comparison Datastax Enterprise vs. Hazelcast vs. Heroic vs. Microsoft Azure Data Explorer vs. Microsoft Azure Table Storage

Editorial information provided by DB-Engines
NameDatastax Enterprise  Xexclude from comparisonHazelcast  Xexclude from comparisonHeroic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparison
DescriptionDataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.A widely adopted in-memory data gridTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchFully managed big data interactive analytics platformA Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelWide column storeKey-value storeTime Series DBMSRelational DBMS infocolumn orientedWide column store
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS
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
Score5.80
Rank#60  Overall
#4  Wide column stores
Score5.97
Rank#57  Overall
#6  Key-value stores
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Websitewww.datastax.com/­products/­datastax-enterprisehazelcast.comgithub.com/­spotify/­heroicazure.microsoft.com/­services/­data-explorerazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationdocs.datastax.comhazelcast.org/­imdg/­docsspotify.github.io/­heroicdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperDataStaxHazelcastSpotifyMicrosoftMicrosoft
Initial release20112008201420192012
Current release6.8, April 20205.3.6, November 2023cloud service with continuous releases
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache 2.0commercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.
Implementation languageJavaJavaJava
Server operating systemsLinux
OS X
All OS with a Java VMhostedhosted
Data schemeschema-freeschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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 indexesyesyesyes infovia Elasticsearchall fields are automatically indexedno
SQL infoSupport of SQLSQL-like DML and DDL statements (CQL); Spark SQLSQL-like query languagenoKusto Query Language (KQL), SQL subsetno
APIs and other access methodsProprietary protocol infoCQL (Cassandra Query Language)
TinkerPop Gremlin infowith DSE Graph
JCache
JPA
Memcached protocol
RESTful HTTP API
HQL (Heroic Query Language, a JSON-based language)
HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP API
Supported programming languagesC
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor ServicesnoYes, possible languages: KQL, Python, Rno
Triggersyesyes infoEventsnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesSharding infono "single point of failure"ShardingShardingSharding infoImplicit feature of the cloud serviceSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesconfigurable replication factor, datacenter aware, advanced replication for edge computingyes infoReplicated Mapyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Tunable Consistency infoconsistency level can be individually decided with each write operation
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoAtomicity and isolation are supported for single operationsone or two-phase-commit; repeatable reads; read commitednonooptimistic locking
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.yesyesnonono
User concepts infoAccess controlAccess rights for users can be defined per objectRole-based access controlAzure Active Directory AuthenticationAccess rights based on private key authentication or shared access signatures
More information provided by the system vendor
Datastax EnterpriseHazelcastHeroicMicrosoft Azure Data ExplorerMicrosoft Azure Table Storage
Specific characteristicsDataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more
Competitive advantagesSupporting the following application requirements: Zero downtime - Built on Apache...
» more
Typical application scenariosApplications that must be massively and linearly scalable with 100% uptime and able...
» more
Key customersCapital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more
Market metricsAmong the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more
Licensing and pricing modelsAnnual subscription
» more

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Datastax EnterpriseHazelcastHeroicMicrosoft Azure Data ExplorerMicrosoft Azure Table Storage
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