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DBMS > Kinetica vs. Snowflake vs. Solr vs. Spark SQL

System Properties Comparison Kinetica vs. Snowflake vs. Solr vs. Spark SQL

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonSnowflake  Xexclude from comparisonSolr  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsCloud-based data warehousing service for structured and semi-structured dataA widely used distributed, scalable search engine based on Apache LuceneSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSSearch engineRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.74
Rank#202  Overall
#99  Relational DBMS
Score71.03
Rank#17  Overall
#11  Relational DBMS
Score57.72
Rank#20  Overall
#3  Search engines
Score22.97
Rank#34  Overall
#20  Relational DBMS
Websitewww.kinetica.comwww.snowflake.comlucene.apache.org/­solrspark.apache.org/­sql
Technical documentationwww.kinetica.com/­docsdocs.snowflake.net/­manuals/­index.htmllucene.apache.org/­solr/­resources.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaSnowflake Computing Inc.Apache Software FoundationApache Software Foundation
Initial release2012201420062014
Current release7.1, August 20218.11.0, November 20213.2.0, October 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++JavaScala
Server operating systemsLinuxhostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
OS X
Windows
Data schemeyesyes infosupport of semi-structured data formats (JSON, XML, Avro)yes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyesyesyes infosupports customizable data types and automatic typingyes
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.noyesyesno
Secondary indexesyesyes infoAll search fields are automatically indexedno
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSolr Parallel SQL InterfaceSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
CLI Client
JDBC
ODBC
Java API
RESTful HTTP/JSON API
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
JavaScript (Node.js)
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsJava pluginsno
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeno infosimilar concept for controling cloud resourcesyes infoUser configurable commands triggered on index changesno
Partitioning methods infoMethods for storing different data on different nodesShardingyesShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyesyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDoptimistic lockingno
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.yes infoGPU vRAM or System RAMnoyesno
User concepts infoAccess controlAccess rights for users and roles on table levelUsers with fine-grained authorization concept, user roles and pluggable authenticationyesno
More information provided by the system vendor
KineticaSnowflakeSolrSpark 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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More resources
KineticaSnowflakeSolrSpark SQL
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