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DBMS > Spark SQL vs. Teradata Aster vs. Tkrzw

System Properties Comparison Spark SQL vs. Teradata Aster vs. Tkrzw

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Editorial information provided by DB-Engines
NameSpark SQL  Xexclude from comparisonTeradata Aster  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionSpark SQL is a component on top of 'Spark Core' for structured data processingPlatform for big data analytics on multistructured data sources and typesA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelRelational DBMSRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitespark.apache.org/­sqldbmx.net/­tkrzw
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationTeradataMikio Hirabayashi
Initial release201420052020
Current release3.5.0 ( 2.13), September 20230.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC++
Server operating systemsLinux
OS X
Windows
LinuxLinux
macOS
Data schemeyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Storeschema-free
Typing infopredefined data types such as float or dateyesyesno
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 infoin Aster File Storeno
Secondary indexesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyesno
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesJava
Python
R
Scala
C
C#
C++
Java
Python
R
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresnoR packagesno
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CoreShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoSQL Map-Reduce Frameworkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynonono
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 persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infousing specific database classes
User concepts infoAccess controlnofine grained access rights according to SQL-standardno

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More resources
Spark SQLTeradata AsterTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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