Introduction to DataBricks
Databricks Workspace Overview
This tutorial provides an introduction to the Databricks environment, focusing on the main components of the workspace essential for data engineering and analytics tasks.
What is Databricks?
Databricks is a cloud-based platform designed for big data and machine learning. It offers a collaborative workspace that integrates seamlessly with Azure, supporting various programming languages such as Python, SQL, Scala, and R. Built on Apache Spark, Databricks facilitates efficient data processing and analytics.
Main Panels in Databricks Workspace
The Databricks interface is organized into several key sections:
- Workspace
- Repos
- Data
- Clusters
1. Workspace
The Workspace panel is your main area for organizing notebooks, libraries, and experiments. It allows you to create folders, notebooks, and dashboards, facilitating collaboration with your team.
2. Repos
The Repos section enables you to connect Git-based repositories such as GitHub or Azure Repos. It's useful for version control, collaborative development, and deployment workflows.
3. Data
The Data panel provides access to mounted storage accounts, databases, tables, and file systems. Here, you can explore datasets, connect to external storage, and manage tables.
4. Clusters
Clusters are virtual machines that Databricks uses to run your code. In this panel, you can create new clusters, monitor running jobs, and manage configurations. Each notebook must be attached to a running cluster.
Learning Outcome
By the end of this module, you will be able to:
- Navigate the Databricks user interface confidently.
- Understand the purpose of the Workspace, Repos, Data, and Clusters panels.
- Use the Workspace to create and organize notebooks.
- Connect to Git repositories using Repos.
- Access and manage data sources through the Data panel.
- Start and manage clusters for running notebooks and jobs.