DBMS > Kinetica vs. Spark SQL
System Properties Comparison Kinetica vs. Spark SQL
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|Editorial information provided by DB-Engines|
|Name||Kinetica Xexclude from comparison||Spark SQL Xexclude from comparison|
|Description||Fully vectorized database across both GPUs and CPUs||Spark SQL is a component on top of 'Spark Core' for structured data processing|
|Primary database model||Relational DBMS||Relational DBMS|
|Secondary database models||Spatial DBMS|
Time Series DBMS
|Developer||Kinetica||Apache Software Foundation|
|Current release||7.1, August 2021||3.4.0 ( 2.13), April 2023|
|License Commercial or Open Source||commercial||Open Source Apache 2.0|
|Cloud-based only Only available as a cloud service||no||no|
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|Implementation language||C, C++||Scala|
|Server operating systems||Linux||Linux|
|Typing predefined data types such as float or date||yes||yes|
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.||no||no|
|SQL Support of SQL||SQL-like DML and DDL statements||SQL-like DML and DDL statements|
|APIs and other access methods||JDBC|
RESTful HTTP API
|Supported programming languages||C++|
|Server-side scripts Stored procedures||user defined functions||no|
|Triggers||yes triggers when inserted values for one or more columns fall within a specified range||no|
|Partitioning methods Methods for storing different data on different nodes||Sharding||yes, utilizing Spark Core|
|Replication methods Methods for redundantly storing data on multiple nodes||Source-replica replication||none|
|MapReduce Offers an API for user-defined Map/Reduce methods||no|
|Consistency concepts Methods to ensure consistency in a distributed system||Immediate Consistency or Eventual Consistency depending on configuration|
|Foreign keys Referential integrity||yes||no|
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data||no||no|
|Concurrency Support for concurrent manipulation of data||yes||yes|
|Durability Support for making data persistent||yes||yes|
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.||yes GPU vRAM or System RAM||no|
|User concepts Access control||Access rights for users and roles on table level||no|
|More information provided by the system vendor|
|Specific characteristics||Native and fully vectorized database across both GPUs and CPUs, with memory first...|
|Competitive advantages||Best in class geospatial and temporal analytics. Lockless architecture provides real-time...|
|Typical application scenarios||Any application requiring (1) real time insights, (2) time series analysis (3) geospatial...|
|Key customers||Citibank, US Air Force, Softbank, OVO, Telkomsel, USPS, 2 of the top 3 US Telcos,...|
|Market metrics||The largest IoT deployment in the world, NORAD, runs on Kinetica.|
|Licensing and pricing models||Always Free Tier in the Cloud, Pay-As-You-Go Consumption Based Pricing, and Portable...|
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