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🔧 How to Create Azure Databricks Service – Step-by-Step Guide

Azure Databricks is one of the most powerful platforms for big data processing and machine learning. It brings together the best of both Apache Spark and Microsoft Azure, providing an optimized and collaborative workspace for analytics teams.

In this tutorial, we’ll guide you through the exact steps to create an Azure Databricks service using the Azure portal.

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🧪 Why Use Azure Databricks?

Before we dive into the steps, here’s why teams prefer Azure Databricks:

• Optimized Apache Spark performance.
• Built-in support for Delta Lake and MLflow.
• Seamless integration with Azure Data Lake, Blob Storage, Event Hubs, and Power BI.
• Highly scalable and collaborative notebooks for data science and engineering.

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🚀 Prerequisites

Before you begin:

• You must have an Azure subscription.
• You need Contributor or higher role in the target Azure resource group.
• Basic understanding of Azure portal and resource groups.

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🛠️ Step-by-Step: Creating Azure Databricks Workspace

🔹 Step 1: Sign in to Azure Portal

Go to https://portal.azure.com and log in with your Azure credentials.

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🔹 Step 2: Search for Databricks

In the search bar at the top, type “Databricks” and select “Azure Databricks” from the search results.

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🔹 Step 3: Click on “Create”

You’ll be redirected to the Azure Databricks page. Click the “Create” button to start the deployment.

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🔹 Step 4: Fill in the Basics

You’ll be asked to provide the following:

• Subscription: Choose your Azure subscription.
• Resource Group: Select an existing resource group or create a new one.
• Workspace Name: Choose a unique name (e.g., my-databricks-dev).
• Region: Pick the Azure region closest to your users/data.

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🔹 Step 5: Choose Pricing Tier

Azure Databricks offers different pricing tiers:

• Standard: Basic functionality for ETL and data exploration.
• Premium: Advanced security and access control.
• Trial: Ideal for experimentation and learning.

Select the one that suits your needs.

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🔹 Step 6: Networking (Optional)

You can customize networking (VNet injection) if required, or leave it as default.

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🔹 Step 7: Review + Create

• Click on Review + Create.
• Azure will validate your inputs.
• Once validated, click on Create.

🚀 Azure will now deploy your Databricks workspace. It may take 3–5 minutes.

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🔑 Step 8: Launch the Workspace

Once deployment is complete:

• Go to the resource.
• Click Launch Workspace to open your Databricks instance.

From here, you can start creating clusters, notebooks, pipelines, and more.

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📦 What’s Next?

Once your workspace is ready, consider exploring:

Feature	Description
🔄 Clusters	Create Spark clusters for data processing
📓 Notebooks	Run PySpark, SQL, Scala, R scripts
💾 Delta Lake	Build reliable lakehouse architecture
📊 Power BI	Connect and visualize your data
🧠 MLflow	Manage your machine learning lifecycle

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🧠 Final Thoughts

Azure Databricks makes big data processing and machine learning fast, secure, and collaborative. With just a few clicks, you can create your own workspace and start building powerful analytics pipelines that scale with your needs.

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