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Databricks System Properties

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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.

Primary database model

Document store
Relational DBMS

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Score	85.60
Rank	#14	Overall
	#2	Document stores
	#9	Relational DBMS

Website

www.databricks.com

Technical documentation

docs.databricks.com

Developer

Databricks

Initial release

2013

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Server operating systems

hosted

Data scheme

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

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

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Supported programming languages

Python
R
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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.

More information provided by the system vendor

Specific characteristics

Supported database models: In addition to the Document store and Relational DBMS listed above, we also support:

Vector: https://docs.databricks.com/en/generative-ai/vector-search.html

Graph: https://docs.databricks.com/en/integrations/graphframes/index.html

Spatial: https://docs.databricks.com/en/sql/language-manual/sql-ref-h3-geospatial-functions.html

Supported programming languages: Python, R, and Scala are the programming languages that can run "within" Databricks. However, just like any other database, one can use virtually any client to connect to Databricks. We provide official drivers in Go, node.js, and Python: https://docs.databricks.com/en/dev-tools/index-driver.html

In-memory capabilities: We started the company initially based on Apache Spark, which is known for its in-memory capabilities, and we certainly support that today. Users can use .cache() API to store data in memory across the cluster for faster computation: https://kb.databricks.com/scala/best-practice-cache-count-take

Related products and services

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

More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2023
2 January 2024, Matthias Gelbmann, Paul Andlinger

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Recent citations in the news

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21 October 2024, Yahoo Finance

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Amazon.com Inc. (NASDAQ:AMZN) Collaborates with Databricks on Generative AI, Aiming for Competitive Edge in Cost and Performance with AWS
21 October 2024, Yahoo Finance

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15 October 2024, The Wall Street Journal

provided by Google News

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