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DBMS > Apache Spark (SQL) vs. Databricks vs. Trino

System Properties Comparison Apache Spark (SQL) vs. Databricks vs. Trino

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonDatabricks  Xexclude from comparisonTrino  Xexclude from comparison
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL
Primary database modelRelational DBMSDocument store
Relational DBMS
Relational DBMS
Secondary database modelsDocument store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score21.62
Rank#29  Overall
#18  Relational DBMS
Score102.66
Rank#12  Overall
#2  Document stores
#8  Relational DBMS
Score5.34
Rank#60  Overall
#34  Relational DBMS
Websitespark.apache.org/­sqlwww.databricks.comtrino.io
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.databricks.comtrino.io/­broadcast
trino.io/­docs/­current
DeveloperApache Software FoundationDatabricksTrino Software Foundation
Initial release201420132012 info2020 rebranded from PrestoSQL
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaJava
Server operating systemsLinux
OS X
Windows
hostedLinux
macOS infofor devlopment
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yes
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.noyesno
Secondary indexesnoyesdepending on connected data-source
SQL infoSupport of SQLSQL-like DML and DDL statementswith Databricks SQLyes
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
RESTful HTTP API
JDBC
RESTful HTTP API
Trino CLI
Supported programming languagesJava
Python
R
Scala
Python
R
Scala
Go
Java
JavaScript (Node.js)
Python
R
Ruby
Server-side scripts infoStored proceduresnouser defined functions and aggregatesyes, depending on connected data-source
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark Coredepending on connected data-source
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyesdepending on connected data-source
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistencydepending on connected data-source
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDdepending on connected data-source
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesdepending on connected data-source
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlnoSQL standard access control
More information provided by the system vendor
Apache Spark (SQL)DatabricksTrino
News

73: Wrapping Trino packages with a bow
9 April 2025

Core Principles and Design Practices of OLAP Engines
27 March 2025

72: Keeping the lake clean
17 March 2025

Twenty four
3 March 2025

71: Fake it real good
27 February 2025

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Apache Spark (SQL)DatabricksTrino
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