Databricks

Skip to main content This browser is no longer supported. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Connect to a Databricks-hosted RStudio Server Article09/10/2024 In this article -->ImportantDatabricks-hosted RStudio Server is deprecated and is only available on Databricks Runtime versions 15.4 and below. For more information, see Hosted RStudio Server deprecation.Use your web browser to sign in to your Azure Databricks workspace and then connect to an Azure Databricks compute that has RStudio Server installed, within that workspace.For hosted RStudio Server, you can use the Open Source Edition or RStudio Workbench (previously RStudio Server Pro) edition on Azure Databricks. If you want to use RStudio Workbench / RStudio Server Pro, you must transfer your existing RStudio Workbench / RStudio Server Pro license to Azure Databricks (see Get started: RStudio Workbench).Databricks recommends that you use Databricks Runtime for Machine Learning (Databricks Runtime ML) on Azure Databricks compute with RStudio Server to reduce compute start times. Databricks Runtime ML includes an unmodified version of the RStudio Server Open Source Edition package. The source code for this package can be found on GitHub. The following table lists the version of RStudio Server Open Source Edition preinstalled on Databricks Runtime ML versions.Databricks Runtime for ML VersionRStudio Server VersionDatabricks Runtime 9.1 LTS ML and 10.4 LTS ML1.4Hosted RStudio Server deprecationDatabricks-hosted RStudio Server is deprecated and is only available on Databricks Runtime 15.4 and below.Support timelineDatabricks will continue supporting hosted RStudio Server on Databricks runtimes 15.4 and below until their end-of-support. For example, Databricks Runtime 15.4 LTS will continue to support hosted RStudio Server until August 19th, 2027.If you need more time to migrate, you can continue using the hosted RStudio Server until your runtime’s end-of-support date. Upgrading to a newer LTS runtime, like 15.4, will extend your support period.Alternative 1: Posit WorkbenchIn partnership with Databricks, Posit PBC provides a Posit Workbench native integration for Databricks.The integration allows you connect to Databricks compute from RStudio Pro, and includes features such as support for Unity Catalog and managed Databricks OAuth credentials.Alternative 2: RStudio DesktopConnect RStudio Desktop to an

This blog post is an introduction of how to use KNIME on Databricks. It's written as a guide, showing you how to connect to a Databricks cluster within KNIME Analytics Platform, as well as looking at several ways to access data from Databricks and upload them back to Databricks.A Guide in 5 SectionsThis "how-to" is divided into the following sections:How to connect to Databricks from KNIMEHow to connect to a Databricks Cluster from KNIMEHow to connect to a Databricks File System from KNIMEReading and Writing Data in DatabricksDatabricks DeltaWhat is Databricks?Databricks is a cloud-based data analytics tool for big data management and large-scale data processing. Developed by the same group behind Apache Spark, the cloud platform is built around Spark, allowing a wide variety of tasks from processing massive amounts of data, building data pipelines across storage file systems, to building machine learning models on a distributed system, all under a unified analytics platform. One advantage of Databricks is the ability to automatically split workload across various machines with on-demand autoscaling.The KNIME Databricks IntegrationKNIME Analytics Platform includes a set of nodes to support Databricks, which is available from version 4.1. This set of nodes is called the KNIME Databricks Integration and enables you to connect to your Databricks cluster running on Microsoft Azure or Amazon AWS cluster. You can access and download the KNIME Databricks Integration from the KNIME Hub.Note: This guide is explained using the paid version of Databricks. The good news is: Databricks also offers a free community edition of Databricks for testing and education purposes, with access to 6 GB clusters, a cluster manager, a notebook environment, and other limited services. If you are using the community edition, you can still follow this guide without any problem.Connect to DatabricksAdd the Databricks JDBC driver to KNIMETo connect to Databricks in KNIME Analytics Platform, first you have to add the Databricks JDBC driver to KNIME with the following steps.1. Download the latest version of the Databricks Simba JDBC driver at the official website. You have to register to be able to download any Databricks drivers. After registering, you will be redirected to the download page with several download links, mostly for ODBC drivers. Download the JDBC Drivers link located at the bottom of the page.Note: If you’re using a Chrome-based web browser and the registration somehow doesn’t work, try to use another web browser, such as Firefox.2. Unzip the compressed file and save it to a folder on your hard disk. Inside the folder, there is another compressed file, unzip this one as well. Inside, you will find a .jar file which is your JDBC driver file.Note: Sometimes you will find several zip files inside the first folder, each file refers to the version of JDBC that is supported by the JDBC driver. KNIME currently supports JDBC drivers that are JDBC 4.1 or JDBC 4.2 compliant.3. Add the new driver to the list of database drivers:In KNIME Analytics Platform, go to File > Preferences > KNIME > Databases and

Documentation.Figure 9. Create Databricks Environment node configuration window.That’s it! After filling all the necessary information in the Create Databricks Environment node, you can execute the node and it will automatically start the cluster if required and wait until the cluster becomes ready. This might take some minutes until the required cloud resources are allocated and all services are started.The node has three output ports:Red port: JDBC connection which allows connecting to KNIME database nodes.Blue port: DBFS connection which allows connecting to remote file handling nodes as well as Spark nodes.Gray port: Spark context which allows connecting to all Spark nodes.The Remote File Handling nodes are available under IO > File Handling > Remote in the node repository.These three output ports allow you to perform a variety of tasks on Databrick clusters via KNIME, such as connecting to a Databricks database and performing database manipulation via KNIME database nodes or executing Spark jobs via KNIME Spark nodes, while pushing down all the computation process into the Databricks cluster.Connect to the Databricks File SystemAnother node in the KNIME Databricks Integration package is called the Databricks File System Connection node. It allows you to connect directly to Databricks File System (DBFS) without having to start a cluster as is the case with the Create Databricks Environment node, which is useful if you simply want to get data in or out of DBFS.In the configuration dialog of this node, you have to provide the domain of the Databricks deployment URL, e.g 1234-5678-abcd.cloud.databricks.com, as well as the access token or username/password as the authentication method. Please check the Connect to a Databricks cluster section for information on how to get the Databricks deployment URL and generate an access token.Figure 10. Databricks File System Connection node configuration windowNote: The Databricks File System Connection node is a part of the KNIME Databricks Integration, available on the KNIME Hub.Reading and Writing Data in DatabricksNow that we are connected to our Databricks cluster, let’s look at the following KNIME example workflow to read data from Databricks, do some basic manipulation via KNIME, and write the result back into Databricks. You can access and download the workflow Connecting to Databricks from the KNIME Hub.Figure 11. The KNIME example workflow (click to enlarge)We are going to read an example dataset flights provided by Databricks. The dataset contains flight trips in the United States during the first three months in 2014.Because the dataset is in CSV format, let’s add the CSV to Spark node, just after the Create Databricks Environment node by connecting it to the DBFS (blue) port and Spark (gray) port. In the configuration window, simply enter the path to the dataset folder, for the flights dataset the path is /databricks-datasets/flights/departuredelays.csv, and then execute the node.The dataset is now available in Spark and you can utilize any number of Spark nodes to perform further data processing visually. In this example, we do a simple grouping by origin airports and calculate the average delay using the Spark GroupBy node.To write the