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DBMS > ClickHouse vs. Hazelcast vs. Microsoft Azure SQL Database vs. Spark SQL

System Properties Comparison ClickHouse vs. Hazelcast vs. Microsoft Azure SQL Database vs. Spark SQL

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Editorial information provided by DB-Engines
NameClickHouse  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionColumn-oriented Relational DBMS powering YandexA widely adopted in-memory data gridDatabase as a Service offering with high compatibility to Microsoft SQL ServerSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value storeRelational DBMSRelational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12Document store
Graph DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.61
Rank#54  Overall
#31  Relational DBMS
Score8.77
Rank#48  Overall
#5  Key-value stores
Score71.36
Rank#15  Overall
#10  Relational DBMS
Score18.74
Rank#34  Overall
#21  Relational DBMS
Websiteclickhouse.techhazelcast.comazure.microsoft.com/­en-us/­services/­sql-databasespark.apache.org/­sql
Technical documentationclickhouse.tech/­docshazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­azure-sqlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperYandex LLCHazelcastMicrosoftApache Software Foundation
Initial release2016200820102014
Current releasev20.11.3.3-stable, November 20204.1.1, December 2020V123.0.1, October 2020
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++JavaC++Scala
Server operating systemsFreeBSD
Linux
macOS
All OS with a Java VMhostedLinux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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 strategyyesno
Secondary indexesnoyesyesno
SQL infoSupport of SQLClose to ANSI SQLSQL-like query languageyesSQL-like DML and DDL statements
APIs and other access methodsHTTP REST
JDBC
ODBC
Proprietary protocol
JCache
JPA
Memcached protocol
RESTful HTTP API
ADO.NET
JDBC
ODBC
JDBC
ODBC
Supported programming languagesC# info3rd party library
C++
Elixir info3rd party library
Go info3rd party library
Java info3rd party library
JavaScript (Node.js) info3rd party library
Kotlin info3rd party library
Nim info3rd party library
Perl info3rd party library
PHP info3rd party library
Python info3rd party library
R info3rd party library
Ruby info3rd party library
Scala info3rd party library
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor ServicesTransact SQLno
Triggersnoyes infoEventsyesno
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated Mapyes, with always 3 replicas availablenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoone or two-phase-commit; repeatable reads; read commitedACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlAccess rights for users and rolesRole-based access controlfine grained access rights according to SQL-standardno
More information provided by the system vendor
ClickHouseHazelcastMicrosoft Azure SQL Database infoformerly SQL AzureSpark SQL
Specific characteristicsHazelcast is an in-memory computing platform that runs applications with extremely...
» more
Competitive advantagesHazelcast has an optimized, multi-threaded architecture that enables the highest...
» more
Typical application scenariosHazelcast powers many types of large-scale, data processing applications. A sample...
» more
Key customersHazelcast customers include 50 of the world’s largest financial services companies,...
» more
Licensing and pricing modelsHazelcast provides both open source and commercial software. The open source offering...
» more

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More resources
ClickHouseHazelcastMicrosoft Azure SQL Database infoformerly SQL AzureSpark SQL
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