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DBMS > Apache Spark (SQL) vs. Yaacomo

System Properties Comparison Apache Spark (SQL) vs. Yaacomo

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Our visitors often compare Apache Spark (SQL) and Yaacomo with Trino, Snowflake and Teradata.

Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score17.39
Rank#32  Overall
#20  Relational DBMS
Websitespark.apache.org/­sqlyaacomo.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationQ2WEB GmbH
Initial release20142009
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScala
Server operating systemsLinux
OS X
Windows
Android
Linux
Windows
Data schemeyesyes
Typing infopredefined data types such as float or dateyesyes
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 indexesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyes
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
Supported programming languagesJava
Python
R
Scala
Server-side scripts infoStored proceduresno
Triggersnoyes
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark Corehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlnofine grained access rights according to SQL-standard

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More resources
Apache Spark (SQL)Yaacomo
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