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DBMS > Apache Druid vs. Quasardb vs. Vertica

System Properties Comparison Apache Druid vs. Quasardb vs. Vertica

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonQuasardb  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 dataDistributed, high-performance timeseries databaseColumnar relational DBMS designed to handle modern analytic workloads, enabling fast query performance
Primary database modelRelational DBMS
Time Series DBMS
Time Series DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.81
Rank#100  Overall
#51  Relational DBMS
#6  Time Series DBMS
Score0.21
Rank#282  Overall
#22  Time Series DBMS
Score20.68
Rank#35  Overall
#21  Relational DBMS
Websitedruid.apache.orgquasardb.netwww.vertica.com
Technical documentationdruid.apache.org/­docs/­latest/­designdoc.quasardb.netwww.vertica.com/­docs/­10.0.x/­HTML/­Content/­Home.htm
DeveloperApache Software Foundation and contributorsquasardbVertica / Micro Focus infoprior to that Hewlett Packard
Initial release201220092005
Current release0.21.0, April 20213.1, January 2019Vertica 10.0 Software, May 2020
Vertica for SQL on Hadoop 10.0 Software, May 2020
License infoCommercial or Open SourceOpen Source infoApache license v2commercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licensescommercial 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++
Server operating systemsLinux
OS X
Unix
BSD
Linux
OS X
Windows
Linux
Data schemeyes infoschema-less columns are supportedschema-freeyes infoUnstructured data can be stored in specific Flex-Tables
Typing infopredefined data types such as float or dateyesyes infointeger and binaryyes
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 indexesyesyes infowith tagsno infoThe concept of 'projections' can be used instead of indexes.
SQL infoSupport of SQLSQL for queryingSQL-like query languageyes infonearly full SQL99 without support for indexes and triggers
APIs and other access methodsJDBC
RESTful HTTP/JSON API
HTTP APIADO.NET
JDBC
Kafka
ODBC
Proprietary protocol
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
C++
Java
Perl
Python
R
Server-side scripts infoStored proceduresnonoyes
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedSharding infoconsistent hashingSharding infoSegmentation, Projection Segmentation, Hash Segmentation
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesSource-replica replication with selectable replication factorMulti-source replication infoCluster level and Object level replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnowith Hadoop integrationyes infowith Vertica Connector for Hadoop
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes infoby using LevelDByes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoTransient mode
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemCryptographically strong user authentication and audit trailfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Apache DruidQuasardbVertica infoVertica Analytics Platform
Specific characteristicsHigh-performance , high-speed, fully integrated timeseries database Seamlessly combines...
» more
Columnar storage, MPP, Hybrid storage model, Aggressive data compression, High availability,...
» more
Competitive advantagesHigh performance in a cost-effective small package Real-time results for even large...
» more
Performance at exabyte scale, Deploy anywhere, Open source innovation, Analysis in...
» more
Typical application scenariosTick database Predictive maintenance Pre-deal checks Large scale monitoring
» more
Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersLarge EU investment banks US based hedge funds Orolia Tellmeplus
» more
Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Market metricsHas been running in very demanding production environments since 2013 Rapid adoption...
» more
Licensing and pricing modelsCommunity edition limited to 2 nodes and 16 GiB of storage per node available for...
» more
Cost-based models and subscription-based models are available
» more

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More resources
Apache DruidQuasardbVertica infoVertica Analytics Platform
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