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DBMS > Amazon Neptune vs. Kinetica vs. Snowflake vs. Spark SQL

System Properties Comparison Amazon Neptune vs. Kinetica vs. Snowflake vs. Spark SQL

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonKinetica  Xexclude from comparisonSnowflake  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudFully vectorized database across both GPUs and CPUsCloud-based data warehousing service for structured and semi-structured dataSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelGraph DBMS
RDF store
Relational DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.56
Rank#109  Overall
#7  Graph DBMS
#5  RDF stores
Score0.74
Rank#202  Overall
#99  Relational DBMS
Score71.03
Rank#17  Overall
#11  Relational DBMS
Score22.97
Rank#34  Overall
#20  Relational DBMS
Websiteaws.amazon.com/­neptunewww.kinetica.comwww.snowflake.comspark.apache.org/­sql
Technical documentationaws.amazon.com/­neptune/­developer-resourceswww.kinetica.com/­docsdocs.snowflake.net/­manuals/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazonKineticaSnowflake Computing Inc.Apache Software Foundation
Initial release2017201220142014
Current release7.1, August 20213.2.0, October 2021
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++Scala
Server operating systemshostedLinuxhostedLinux
OS X
Windows
Data schemeschema-freeyesyes infosupport of semi-structured data formats (JSON, XML, Avro)yes
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.nonoyesno
Secondary indexesnoyesno
SQL infoSupport of SQLnoSQL-like DML and DDL statementsyesSQL-like DML and DDL statements
APIs and other access methodsRDF 1.1 / SPARQL 1.1
TinkerPop Gremlin 3.3
JDBC
ODBC
RESTful HTTP API
CLI Client
JDBC
ODBC
JDBC
ODBC
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
C++
Java
JavaScript (Node.js)
Python
JavaScript (Node.js)
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsuser defined functionsno
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeno infosimilar concept for controling cloud resourcesno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingyesyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones high availability, asynchronous replication for up to 15 read replicasSource-replica replicationyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyes infoRelationships in graphsyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMnono
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Access rights for users and roles on table levelUsers with fine-grained authorization concept, user roles and pluggable authenticationno
More information provided by the system vendor
Amazon NeptuneKineticaSnowflakeSpark SQL
Specific characteristicsNative and fully vectorized database across both GPUs and CPUs, with memory first...
» more
Competitive advantagesBest in class geospatial and temporal analytics. Lockless architecture provides real-time...
» more
Typical application scenariosAny application requiring (1) real time insights, (2) time series analysis (3) geospatial...
» more
Key customersCitibank, US Air Force, Softbank, OVO, Telkomsel, USPS, 2 of the top 3 US Telcos,...
» more
Market metricsThe largest IoT deployment in the world, NORAD, runs on Kinetica.
» more
Licensing and pricing modelsAlways Free Tier in the Cloud, Pay-As-You-Go Consumption Based Pricing, and Portable...
» 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 NeptuneKineticaSnowflakeSpark SQL
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