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DBMS > Amazon Aurora vs. ClickHouse vs. Datastax Enterprise vs. Spark SQL

System Properties Comparison Amazon Aurora vs. ClickHouse vs. Datastax Enterprise vs. Spark SQL

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Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonClickHouse  Xexclude from comparisonDatastax Enterprise  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonColumn-oriented Relational DBMS powering YandexDataStax 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.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSWide column storeRelational DBMS
Secondary database modelsDocument storeDocument store
Graph DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.97
Rank#47  Overall
#28  Relational DBMS
Score4.40
Rank#71  Overall
#39  Relational DBMS
Score8.09
Rank#46  Overall
#4  Wide column stores
Score16.93
Rank#35  Overall
#21  Relational DBMS
Websiteaws.amazon.com/­rds/­auroraclickhouse.techwww.datastax.com/­products/­datastax-enterprisespark.apache.org/­sql
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmlclickhouse.tech/­docsdocs.datastax.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazonYandex LLCDataStaxApache Software Foundation
Initial release2015201620112014
Current releasev19.13.3.26, August 20196.7, December 20182.4.5, February 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaScala
Server operating systemshostedLinux
OS X
Linux
OS X
Windows
Data schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes
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.yesnono
Secondary indexesyesyesno
SQL infoSupport of SQLyesSQL-like DML and DDL statements (CQL); Spark SQLSQL-like DML and DDL statements
APIs and other access methodsADO.NET
JDBC
ODBC
Proprietary protocol infoCQL (Cassandra Query Language)
TinkerPop Gremlin infowith DSE Graph
JDBC
ODBC
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesnono
Triggersyesyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningSharding infono "single point of failure"yes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMaster-slave replicationconfigurable replication factor, datacenter aware, advanced replication for edge computingnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Tunable Consistency infoconsistency level can be individually decided with each write operation
Foreign keys infoReferential integrityyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infoAtomicity and isolation are supported for single operationsno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users can be defined per objectno
More information provided by the system vendor
Amazon AuroraClickHouseDatastax EnterpriseSpark SQL
Specific characteristics100% continuous availability, real-time, distributed, massive scale data management...
» more
Competitive advantagesSupporting the following application requirements: Contextual: complete data layer...
» more
Typical application scenariosApplications that must serve contextual information and be always-on, distributed,...
» 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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Amazon AuroraClickHouseDatastax EnterpriseSpark SQL
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