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

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

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 comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
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 SparkPlatform for big data analytics on multistructured data sources and types
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSTime Series DBMSRelational DBMSRelational 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
Score9.06
Rank#46  Overall
#29  Relational DBMS
Score0.14
Rank#332  Overall
#29  Time Series DBMS
Score0.54
Rank#250  Overall
#114  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 MachineTeradata
Initial release20122000200920142005
Current release29.0.1, April 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 availablecommercial
Cloud-based only infoOnly available as a cloud servicenonononono
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
Linux
Data schemeyes infoschema-less columns are supportedyesschema-freeyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesyesyes infointeger and binaryyesyes
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 infoin Aster File Store
Secondary indexesyesyesyes infowith tagsyesyes
SQL infoSupport of SQLSQL for queryingyesSQL-like query languageyesyes
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
OLE DB
HTTP APIJDBC
Native Spark Datasource
ODBC
ADO.NET
JDBC
ODBC
OLE DB
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
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresnoyesnoyes infoJavaR packages
Triggersnononoyesno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingSharding infoconsistent hashingShared Nothhing Auto-Sharding, Columnar PartitioningSharding
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
yes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyeswith Hadoop integrationYes, via Full Spark Integrationyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyes infoby using LevelDByesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoTransient modeyesno
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-standardfine grained access rights according to SQL-standard

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More resources
Apache DruidNetezza infoAlso called PureData System for Analytics by IBMQuasardbSplice MachineTeradata Aster
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