🌐Getting Started with Azure: Creating an Account, Subscription, Resource Group, and Storage Account, Link services

 

🚀 Getting Started with Azure: Creating an Account, Subscription, Resource Group, and Storage Account

Azure is a powerful cloud platform that provides a range of services for managing, storing, and processing data. Before utilizing Azure services, you need to follow four essential steps:

1. 🆕 Create an Azure Account
2. 📝 Set Up a Subscription
3. 📦 Create a Resource Group
4. 🗂️ Use Resources (e.g., Storage Account)

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🛠️ Step 1: Creating a Resource Group in Azure

A Resource Group in Azure is a logical container that holds related Azure resources, allowing for easy management, organization, and access control. Any resource in Azure must belong to a resource group.

📌 How to Create a Resource Group?

• 🔍 Search for "Resource Group" in the Azure search bar.
• 🏢 Click on "Create Resource Group" and provide a name.
• ✅ Click "Create" to finalize the resource group.

Alternative Method: While creating a Storage Account, you can create a new Resource Group:

• Click on "Create New" during storage account setup.
• Enter a name, e.g., divyalearn_az.
• Click "Create".

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🛠️ Step 2: Creating a Storage Account in Azure

A Storage Account in Azure allows you to store data in various formats, such as Blob Storage, Table Storage, File Shares, and Queues.

📊 How to Create a Storage Account?

1. 🔍 Search for "Storage Account" in the Azure search bar.
2. 👉 Click "Create Storage Account".
3. 📦 Attach the existing resource group (divyalearn_az).
4. 📝 Fill in the Instance Details:
   • Storage Account Name: divyabucket02 (must be unique, lowercase, and can include numbers).
   • 🌍 Region: Choose any location, e.g., USA.
   • 🎨 Primary Services:
     • Azure Blob Storage (for unstructured data backup like WhatsApp media, etc.)
     • Azure Delta Lake Storage (for big data and data science analysis, as it supports structured and semi-structured data.)
5. 🌐 Performance Options:
   • Standard (default for general use)
   • Premium (recommended for production workloads)
6. 🏙️ Redundancy:
   • Locally Redundant Storage (LRS): Data is stored in a single location (low-cost, good for practice).
   • Geo-Redundant Storage (GRS): Data is stored across multiple locations (recommended for real-time scenarios, reduces data loss risk but is costly).
7. 🏰 Enable Hierarchical Namespace:
   • ✅ If enabled, it is considered Data Lake Storage (used in 80% of office use cases).
   • ❌ If disabled, it remains Blob Storage (suitable for general-purpose file storage).
8. ✅ Click "Review + Create" and finalize the setup.

📂 Working with Containers

Once the Storage Account is created:

• Navigate to Storage Account → Data Storage → Containers → Click + Container.
• Provide a container name, e.g., input or output (similar to folders in AWS S3 buckets).
• ✅ Click "Create".
• 💾 Upload data by dragging and dropping files into the container (e.g., 1000record.csv and asl.csv).

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🌌 Step 3: Processing Data Using Azure Data Factory

🛠️ Creating an Azure Data Factory

1. 🔍 Search for "Data Factory" in Azure.
2. 👉 Click "Create Data Factory".
3. 💏 Select the Free Tier.
4. 📦 Attach the resource group (divyalearn_az).
5. 🏷️ Provide a name, e.g., learnazure (lowercase, no special characters or numbers).
6. ✅ Click "Review + Create".

👤 Linking Azure Data Lake Storage to Data Factory

1. Navigate to Azure Data Factory → Launch Studio.
2. In the Manage section (4th option on the left panel):
• Click Linked Services → + New Linked Service.
• Select Azure Data Lake Storage Gen2.
• Provide a name, e.g., myfirstlinkservice (Ls_connect_adl for company standard names).
• Choose the subscription (Free Tier).
• Select the Storage Account (divyabucket02).
• ✅ Click "Create".
  

🛠️ Creating a Dataset in Data Factory

1. Navigate to Author (pencil icon on the left panel).
2. Click "Dataset" → + New Dataset.
3. Set the properties:
• Name: Ds_adharcardata.
• Linked Service: Ls_connect_adl.
• File Path: Drag and drop input/adharcard.csv.

🎯 Creating a Data Flow to Process Data

1. In Author, click "Data Flow" → + New Data Flow".
2. Set properties:
   • Name: ds_processadhar.
   • Click Add Source → Arrow → Add Source.
3. Configure Source Settings:
   • Output Stream Name: input_source1.
   • Source Type: Dataset.
   • Dataset: Ds_adharcardata.
   • ✅ Enable Dataflow Debug (to preview data for one hour).
4. Click + Add Filter Condition to process specific data:
   • Select Filter On.
   • Click Expression Builder.
   • Add condition: age > 35.
5. Click + Add Sink to store processed data.

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📄 Conclusion

By following these steps, you have successfully:

1. Created an Azure Resource Group.
2. Set up an Azure Storage Account with containers.
3. Created an Azure Data Factory and linked it to Data Lake Storage.
4. Processed a dataset (adharcard.csv) using Data Flow with a filter (age > 35).
5. Stored the processed data using Sink in Data Factory.

These fundamental steps help in managing and processing data efficiently using Azure cloud services. 💪🚀