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DBMS > Apache Druid vs. Pinecone vs. Valentina Server vs. Vertica

System Properties Comparison Apache Druid vs. Pinecone vs. Valentina Server vs. Vertica

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonPinecone  Xexclude from comparisonValentina Server  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataA managed, cloud-native vector databaseObject-relational database and reports serverCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelRelational DBMS
Time Series DBMS
Vector DBMSRelational DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
Score3.16
Rank#95  Overall
#2  Vector DBMS
Score0.17
Rank#327  Overall
#145  Relational DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitedruid.apache.orgwww.pinecone.iowww.valentina-db.netwww.vertica.com
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.pinecone.io/­docs/­overviewvalentina-db.com/­docs/­dokuwiki/­v5/­doku.phpvertica.com/­documentation
DeveloperApache Software Foundation and contributorsPinecone Systems, IncParadigma SoftwareOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2012201919992005
Current release29.0.1, April 20245.7.512.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenoyesnono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++
Server operating systemsLinux
OS X
Unix
hostedLinux
OS X
Windows
Linux
Data schemeyes infoschema-less columns are supportedyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesString, Number, Booleanyesyes
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.nonono
Secondary indexesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL for queryingnoyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBC
RESTful HTTP/JSON API
RESTful HTTP APIODBCADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
Python.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoyesyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedhorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.nonoyesno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Apache DruidPineconeValentina ServerVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» 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 both available. One license is...
» more

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More resources
Apache DruidPineconeValentina ServerVertica infoOpenText™ Vertica™
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