DBMS > Databricks vs. Microsoft SQL Server vs. Oracle Rdb vs. Snowflake vs. Vitess

System Properties Comparison Databricks vs. Microsoft SQL Server vs. Oracle Rdb vs. Snowflake vs. Vitess

Editorial information provided by DB-Engines

Name

Description

The 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.

Microsofts flagship relational DBMS

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

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

www.databricks.com

www.microsoft.com/en-us/sql-server

www.oracle.com/database/technologies/related/rdb.html

www.snowflake.com

vitess.io

Technical documentation

docs.databricks.com

learn.microsoft.com/en-US/sql/sql-server

www.oracle.com/database/technologies/related/rdb-doc.html

docs.snowflake.net/manuals/index.html

vitess.io/docs

Developer

Databricks

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

Snowflake Computing Inc.

The Linux Foundation, PlanetScale

Initial release

2013

1989

1984

2014

2013

Current release

SQL Server 2022, November 2022

7.4.1.1, 2021

15.0.2, December 2022

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Server operating systems

hosted

Linux
Windows

HP Open VMS

hosted

Docker
Linux
macOS

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

Flexible Schema (defined schema, partial schema, schema free)

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

with Databricks SQL

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

CLI Client
JDBC
ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Python
R
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

user defined functions

yes

proprietary syntax

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

tables can be distributed across several files (horizontal partitioning); sharding through federation

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

yes

Multi-source replication
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

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

yes, on a single node

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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