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DBMS > Spark SQL vs. STSdb vs. Teradata vs. Yaacomo

System Properties Comparison Spark SQL vs. STSdb vs. Teradata vs. Yaacomo

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Editorial information provided by DB-Engines
NameSpark SQL  Xexclude from comparisonSTSdb  Xexclude from comparisonTeradata  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionSpark SQL is a component on top of 'Spark Core' for structured data processingKey-Value Store with special method for indexing infooptimized for high performance using a special indexing methodA hybrid cloud data analytics software platform (Teradata Vantage)OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelRelational DBMSKey-value storeRelational 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
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.04
Rank#360  Overall
#52  Key-value stores
Score45.33
Rank#21  Overall
#15  Relational DBMS
Websitespark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4www.teradata.comyaacomo.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.teradata.com
DeveloperApache Software FoundationSTS Soft SCTeradataQ2WEB GmbH
Initial release2014201119842009
Current release3.5.0 ( 2.13), September 20234.0.8, September 2015Teradata Vantage 1.0 MU2, January 2019
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoGPLv2, 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 languageScalaC#
Server operating systemsLinux
OS X
Windows
Windowshosted
Linux
Android
Linux
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyes infoprimitive types and user defined types (classes)yesyes
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.noyesno
Secondary indexesnonoyes infoJoin-index to prejoin tables, aggregate index, sparse index, hash indexyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoyes infoSQL 2016 + extensionsyes
APIs and other access methodsJDBC
ODBC
.NET Client API.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB
JDBC
ODBC
Supported programming languagesJava
Python
R
Scala
C#
Java
C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby
Server-side scripts infoStored proceduresnonoyes infoUDFs, stored procedures, table functions in parallel
Triggersnonoyesyes
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CorenoneSharding infoHashinghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnonenoneMulti-source replication
Source-replica replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.noyesyes
User concepts infoAccess controlnonofine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Spark SQLSTSdbTeradataYaacomo
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