DBMS > Apache Spark (SQL) vs. Linter

System Properties Comparison Apache Spark (SQL) vs. Linter

Please select another system to include it in the comparison.

Our visitors often compare Apache Spark (SQL) and Linter with SQLite, PostgreSQL and Microsoft SQL Server.

Editorial information provided by DB-Engines

Name

Description

Apache Spark SQL is a component on top of 'Spark Core' for structured data processing

RDBMS for high security requirements

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	21.62
Rank	#29	Overall
	#18	Relational DBMS

Score	0.00
Rank	#385	Overall
	#157	Relational DBMS

Website

spark.apache.org/sql

linter.ru

Technical documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation

relex.ru

Initial release

2014

1990

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Scala

C and C++

Server operating systems

Linux
OS X
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Supported programming languages

Java
Python
R
Scala

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Introducing AWS Glue 5.0 for Apache Spark
4 December 2024, Amazon Web Services (AWS)

Scala vs Python for Apache Spark: An In-depth Comparison With Use Cases For Each
21 April 2025, Simplilearn.com

How to run Pandas code on Spark
25 January 2025, Theodo Data & AI

The 6 Best Apache Spark Courses on Udemy to Consider for 2025
1 January 2025, solutionsreview.com

18 top big data tools and technologies to know about in 2025
22 January 2025, TechTarget

provided by Google News

Share this page