Azure Databricks Tutorial


           Azure Databricks Tutorial

  • Here’s a breakdown of how to start with Azure Databricks, including key concepts and a hands-on tutorial.

    Understanding Azure Databricks

    • Core Purpose:  Azure Databricks is a cloud-based service built upon Apache Spark. At scale, it’s designed for streamlined data engineering, data science, and machine learning.
      • Advantages:Collaboration: Easy workspace sharing for teams.
      • Managed Infrastructure: Azure handles the setup and maintenance of Spark clusters for you.
      • Scalability: Handle massive datasets with ease.
      • Integration: Connects with Azure Blob Storage, Azure Data Lake Store, and other Azure services.

    Tutorial: Getting Started


    • An Azure subscription (if you don’t have one, you can create a free trial account)


    1. Create a Databricks Workspace:
      • Log in to the Azure portal (
      • Find “Azure Databricks” in the search bar.
      • Click “Create” to start the setup wizard.
        • Set up: Workspace name
        • Resource group
        • Region
        • Pricing tier (Standard or Premium)
    2. Create a Cluster:
      • Navigate to your Databricks workspace.
      • Go to the “Clusters” tab.
        • Click “Create Cluster” and provide the cluster name
        • Databricks runtime version (choose one with ML libraries for machine learning)
        • Worker and driver node types (hardware choices)
    3. Create a Notebook:
      • In your workspace, go to the “Workspace” tab.
      • Click “Create” and then “Notebook”.
      • Name your notebook, choose a default language (Python, Scala, SQL, or R), and attach it to your cluster.
    4. Basic Exploration:
      • Load Sample Data: Databricks come with pre-loaded datasets. Type the following in a notebook cell and press Shift+Enter to run it:
      • Python
      • df =“csv”).option(“header”, “true”).load(“/databricks-datasets/samples/population-vs-price/data_geo.csv”)
      • display(df) 
      • Run SQL Queries: Databricks supports SQL for data exploration:
    5. SQL
    6. SELECT * FROM df WHERE state = ‘CA’

    Key Concepts to Deepen Your Learning

    • DataFrames: The core data structure in Spark is similar to tables.
    • Spark SQL: Using SQL-like syntax for powerful data transformations and analysis.
    • Delta Lake: An open-source storage layer that provides reliability and ACID transactions on your data lake.
    • MLlib: Databricks’ machine learning library for algorithms and model building.

Databricks Training Demo Day 1 Video:

You can find more information about Databricks Training in this Dtabricks Docs Link



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