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DBMS > Apache Druid vs. Netezza vs. Quasardb vs. Splice Machine

System Properties Comparison Apache Druid vs. Netezza vs. Quasardb vs. Splice Machine

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonQuasardb  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataData warehouse and analytics appliance part of IBM PureSystemsDistributed, high-performance timeseries databaseOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSTime Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.33
Rank#99  Overall
#51  Relational DBMS
#7  Time Series DBMS
Score11.20
Rank#46  Overall
#29  Relational DBMS
Score0.19
Rank#330  Overall
#30  Time Series DBMS
Score0.54
Rank#257  Overall
#118  Relational DBMS
Websitedruid.apache.orgwww.ibm.com/­products/­netezzaquasar.aisplicemachine.com
Technical documentationdruid.apache.org/­docs/­latest/­designdoc.quasar.ai/­mastersplicemachine.com/­how-it-works
DeveloperApache Software Foundation and contributorsIBMquasardbSplice Machine
Initial release2012200020092014
Current release29.0.0, February 20243.14.1, January 20243.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licensesOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Java
Server operating systemsLinux
OS X
Unix
Linux infoincluded in applianceBSD
Linux
OS X
Windows
Linux
OS X
Solaris
Windows
Data schemeyes infoschema-less columns are supportedyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes 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.nono
Secondary indexesyesyesyes infowith tagsyes
SQL infoSupport of SQLSQL for queryingyesSQL-like query languageyes
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
OLE DB
HTTP APIJDBC
Native Spark Datasource
ODBC
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C
C++
Fortran
Java
Lua
Perl
Python
R
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnoyesnoyes infoJava
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingSharding infoconsistent hashingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesSource-replica replicationSource-replica replication with selectable replication factorMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyeswith Hadoop integrationYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infoby using LevelDByes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoTransient modeyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemUsers with fine-grained authorization conceptCryptographically strong user authentication and audit trailAccess rights for users, groups and roles according to SQL-standard

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More resources
Apache DruidNetezza infoAlso called PureData System for Analytics by IBMQuasardbSplice Machine
Recent citations in the news

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