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DBMS > Apache Druid vs. Kinetica vs. Vertica

System Properties Comparison Apache Druid vs. Kinetica vs. Vertica

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonKinetica  Xexclude from comparisonVertica infoVertica Analytics Platform  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataGPU-accelerated database for real-time analysis of large and streaming datasetsColumnar relational DBMS designed to handle modern analytic workloads, enabling fast query performance
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.69
Rank#103  Overall
#51  Relational DBMS
#7  Time Series DBMS
Score0.74
Rank#195  Overall
#97  Relational DBMS
Score20.95
Rank#33  Overall
#20  Relational DBMS
Websitedruid.apache.orgwww.kinetica.comwww.vertica.com
Technical documentationdruid.apache.org/­docs/­latest/­designwww.kinetica.com/­docswww.vertica.com/­docs/­10.0.x/­HTML/­Content/­Home.htm
DeveloperApache Software Foundation and contributorsKineticaVertica / Micro Focus infoprior to that Hewlett Packard
Initial release201220122005
Current release0.20.1, January 20216.0Vertica 10.0 Software, May 2020
Vertica for SQL on Hadoop 10.0 Software, May 2020
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercial 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 languageJavaC, C++
Server operating systemsLinux
OS X
Unix
LinuxLinux
Data schemeyes infoschema-less columns are supportedyesyes 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 indexesyesyesno infoThe concept of 'projections' can be used instead of indexes.
SQL infoSupport of SQLSQL for queryingSQL-like DML and DDL statementsyes infonearly full SQL99 without support for indexes and triggers
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
Kafka
ODBC
Proprietary protocol
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C++
Java
JavaScript (Node.js)
Python
C++
Java
Perl
Python
R
Server-side scripts infoStored proceduresnouser defined functionsyes
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingSharding infoSegmentation, Projection Segmentation, Hash Segmentation
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesSource-replica replicationMulti-source replication infoCluster level and Object level replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infowith Vertica Connector for Hadoop
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyesyes
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.noyes infoGPU vRAM or System RAM
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users and roles on table levelfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Apache DruidKineticaVertica infoVertica Analytics Platform
Specific characteristicsColumnar storage, MPP, Hybrid storage model, Aggressive data compression, High availability,...
» more
Competitive advantagesPerformance at exabyte scale, Deploy anywhere, Open source innovation, Analysis in...
» more
Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are available
» more

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More resources
Apache DruidKineticaVertica infoVertica Analytics Platform
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