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DBMS > Apache Druid vs. Splice Machine vs. Teradata vs. Valentina Server

System Properties Comparison Apache Druid vs. Splice Machine vs. Teradata vs. Valentina Server

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonSplice Machine  Xexclude from comparisonTeradata  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkA hybrid cloud data analytics software platform (Teradata Vantage)Object-relational database and reports server
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial 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
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score45.33
Rank#21  Overall
#15  Relational DBMS
Score0.17
Rank#327  Overall
#145  Relational DBMS
Websitedruid.apache.orgsplicemachine.comwww.teradata.comwww.valentina-db.net
Technical documentationdruid.apache.org/­docs/­latest/­designsplicemachine.com/­how-it-worksdocs.teradata.comvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperApache Software Foundation and contributorsSplice MachineTeradataParadigma Software
Initial release2012201419841999
Current release29.0.1, April 20243.1, March 2021Teradata Vantage 1.0 MU2, January 20195.7.5
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoAGPL 3.0, commercial license availablecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava
Server operating systemsLinux
OS X
Unix
Linux
OS X
Solaris
Windows
hosted
Linux
Linux
OS X
Windows
Data schemeyes infoschema-less columns are supportedyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.noyes
Secondary indexesyesyesyes infoJoin-index to prejoin tables, aggregate index, sparse index, hash indexyes
SQL infoSupport of SQLSQL for queryingyesyes infoSQL 2016 + extensionsyes
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
Native Spark Datasource
ODBC
.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB
ODBC
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby
.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresnoyes infoJavayes infoUDFs, stored procedures, table functions in parallelyes
Triggersnoyesyesyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShared Nothhing Auto-Sharding, Columnar PartitioningSharding infoHashing
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesMulti-source replication
Source-replica replication
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoYes, via Full Spark Integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yes
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.noyesyesyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users, groups and roles according to SQL-standardfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Apache DruidSplice MachineTeradataValentina Server
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