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DBMS > Kinetica vs. Spark SQL vs. Vertica

System Properties Comparison Kinetica vs. Spark SQL vs. Vertica

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparisonVertica infoVertica Analytics Platform  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsSpark SQL is a component on top of 'Spark Core' for structured data processingColumnar relational DBMS designed to handle modern analytic workloads, enabling fast query performance
Primary database modelRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.73
Rank#205  Overall
#101  Relational DBMS
Score21.87
Rank#34  Overall
#20  Relational DBMS
Score20.45
Rank#35  Overall
#21  Relational DBMS
Websitewww.kinetica.comspark.apache.org/­sqlwww.vertica.com
Technical documentationwww.kinetica.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.vertica.com/­docs/­10.0.x/­HTML/­Content/­Home.htm
DeveloperKineticaApache Software FoundationVertica / Micro Focus infoprior to that Hewlett Packard
Initial release201220142005
Current release7.1, August 20213.1.1, March 2021Vertica 10.0 Software, May 2020
Vertica for SQL on Hadoop 10.0 Software, May 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoAvailable on all major clouds - Amazon AWS, Microsoft Azure and Google Cloud Platform
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++Scala
Server operating systemsLinuxLinux
OS X
Windows
Linux
Data schemeyesyesyes infoUnstructured data can be stored in specific Flex-Tables
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.nonoyes
Secondary indexesyesnono infoThe concept of 'projections' can be used instead of indexes.
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like DML and DDL statementsyes infonearly full SQL99 without support for indexes and triggers
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
ADO.NET
JDBC
Kafka
ODBC
Proprietary protocol
RESTful HTTP API
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C++
Java
Perl
Python
R
Server-side scripts infoStored proceduresuser defined functionsnoyes
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreSharding infoSegmentation, Projection Segmentation, Hash Segmentation
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnoneMulti-source replication infoCluster level and Object level replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infowith Vertica Connector for Hadoop
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
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.yes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess rights for users and roles on table levelnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
KineticaSpark SQLVertica infoVertica Analytics Platform
Specific characteristicsNative and fully vectorized database across both GPUs and CPUs, with memory first...
» more
Columnar storage, MPP, Hybrid storage model, Aggressive data compression, High availability,...
» more
Competitive advantagesBest in class geospatial and temporal analytics. Lockless architecture provides real-time...
» more
Performance at exabyte scale, Deploy anywhere, Open source innovation, Analysis in...
» more
Typical application scenariosAny application requiring (1) real time insights, (2) time series analysis (3) geospatial...
» more
Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersCitibank, US Air Force, Softbank, OVO, Telkomsel, USPS, 2 of the top 3 US Telcos,...
» more
Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» 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
Cost-based models and subscription-based models are available
» more

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Related products and services
3rd partiesSQLFlow: Provides a visual representation of the overall flow of data. Automated SQL data lineage analysis across Databases, ETL, Business Intelligence, Cloud and Hadoop environments by parsing SQL Script and stored procedure.
» more

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More resources
KineticaSpark SQLVertica infoVertica Analytics Platform
Recent citations in the news

Newswire & Press Release / Curtiss-Wright Adds Kinetica Software Support to the PacStar® 453 GPU Module for Intelligent Sensor Data Analytics - Electronics / Instrumentation / RFID - Curtiss-Wright Corporation | NewswireToday
22 September 2021, Newswire Today

United States GPU Database Market Size, Growth And Analysis By Leading Keyplayers – Kinetica, Omnisci, Sqream, Neo4j, Nvidia, Brytlyt, Jedox – Stillwater Current
20 September 2021, Stillwater Current

Preventing the Next 9/11 Goal of NORAD's New Streaming Data Warehouse
30 August 2021, Datanami

Kinetica Fuses Streaming and Contextual Analysis At Scale
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19 September 2021, Madison.com

Beginner's Guide To Machine Learning With Apache Spark
16 July 2021, Analytics India Magazine

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Enterprise Database Management System (DBMS) Market Biggest Demand with Growth and Key Players Analysis – Oracle, Microsoft, IBM, SAP, AWS, MongoDB, Google Cloud – Stillwater Current
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