DBMS > Microsoft Azure SQL Database vs. Snowflake vs. Spark SQL

System Properties Comparison Microsoft Azure SQL Database vs. Snowflake vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Database as a Service offering with high compatibility to Microsoft SQL Server

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	78.40
Rank	#15	Overall
	#10	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

azure.microsoft.com/en-us/products/azure-sql/database

www.snowflake.com

spark.apache.org/sql

Technical documentation

docs.microsoft.com/en-us/azure/azure-sql

docs.snowflake.net/manuals/index.html

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

Developer

Microsoft

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2010

2014

Current release

V12

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Scala

Server operating systems

hosted

Linux
OS X
Windows

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

Transact SQL

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with always 3 replicas available

yes

none

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

Users with fine-grained authorization concept, user roles and pluggable authentication

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