Then, the logical representation of the job is sent to the Spark server running in Azure Databricks for execution in the cluster. It allows you to write jobs using Spark APIs and run them remotely on an Azure Databricks cluster instead of in the local Spark session.įor example, when you run the DataFrame command ("parquet").load(.).groupBy(.).agg(.).show() using Databricks Connect, the parsing and planning of the job runs on your local machine. ![]() Overviewĭatabricks Connect is a client library for Databricks Runtime. This article explains how Databricks Connect works, walks you through the steps to get started with Databricks Connect, explains how to troubleshoot issues that may arise when using Databricks Connect, and differences between running using Databricks Connect versus running in an Azure Databricks notebook. ![]() Also, be aware of the limitations of Databricks Connect.ĭatabricks Connect allows you to connect your favorite IDE (Eclipse, IntelliJ, P圜harm, RStudio, Visual Studio Code), notebook server (Jupyter Notebook, Zeppelin), and other custom applications to Azure Databricks clusters. ![]() Databricks plans no new feature development for Databricks Connect at this time. Databricks recommends that you use dbx by Databricks Labs for local development instead of Databricks Connect.
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