Azure Cli Scenarios

Azure CLI Scenarios

🔹 Exercise 1: Setup & Resource Groups

Login to Azure CLI

az login

Set your active subscription

az account list --output table
az account set --subscription "<your-subscription-id>"

Create a resource group

az group create --name MyRG --location eastus

Verify it

az group list --output table

🔹 Exercise 2: Storage Account + Blob

Create a storage account

az storage account create \
  --name mystorage123xyz --resource-group MyRG \
  --location eastus --sku Standard_LRS --kind StorageV2

Create a container

az storage container create \
  --account-name mystorage123xyz --name rawdata

Upload a CSV file

echo "id,name,age
1,Alice,30
2,Bob,25
3,Charlie,28" > sample.csv

az storage blob upload --account-name storageaccountfromclivb \
--container-name clidatacontainer --name sample.csv --file ./sample.csv

List blobs

az storage blob list \
  --account-name storageaccountfromclivb --container-name clidatacontainer \
  --output table

🔹 Exercise 3: SQL Database

Create a SQL server

az sql server create \
  --name my-sql-server123 --resource-group MyRG \
  --location eastus --admin-user myadmin --admin-password MyP@ssw0rd!

Create a database

az sql db create \
  --resource-group MyRG --server my-sql-server123 \
  --name mydb --service-objective S0

Show details

az sql db show --resource-group MyRG --server my-sql-server123 --name mydb

🔹 Exercise 4: Virtual Machine

Create a Linux VM

az vm create \
  --resource-group MyRG --name MyVM \
  --image UbuntuLTS --admin-username azureuser --generate-ssh-keys

Check running VMs

az vm list --output table

Stop the VM

az vm stop --resource-group MyRG --name MyVM

Start it again

az vm start --resource-group MyRG --name MyVM

🔹 Exercise 5: Build a Mini Data Lake Pipeline

Goal: Create a Data Lake storage, upload raw data, then use queries to check it.

1. Create a storage account with hierarchical namespace (Data Lake Gen2):

az storage account create \
  --name mydatalake123 --resource-group MyRG \
  --location eastus --sku Standard_LRS --kind StorageV2 \
  --hierarchical-namespace true

Create a container for raw data:

az storage container create \
  --account-name mydatalake123 --name raw

Upload multiple files (CSV, JSON):

for file in ./data/*; do
  az storage blob upload \
    --account-name mydatalake123 \
    --container-name raw \
    --name $(basename $file) \
    --file $file
done

basename $file

• basename is a Unix command.
• It strips the directory path from a file and returns only the filename.
• Example:

file="/home/user/docs/report.pdf"
basename $file

Output:

report.pdf

--name $(basename $file)

• The $(...) syntax means command substitution: run the command inside and replace it with its output.
• So $(basename $file) gets replaced by the filename (without path).
• If file="/home/user/docs/report.pdf", then:

--name $(basename $file)

becomes:

--name report.pdf

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Why it’s used

Often, scripts loop through files in directories. Instead of passing full paths, they just want the filename for naming a resource, argument, or output.

Example in Azure CLI:

for file in /path/to/files/*; do
  az storage blob upload \
    --account-name mystorage \
    --container-name mycontainer \
    --file $file \
    --name $(basename $file)
done

Here:

• --file $file → full path to the file.
• --name $(basename $file) → just the filename in the blob storage.

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Verify using query:

az storage blob list \
  --account-name mydatalake123 --container-name raw \
  --query "[].{Name:name, Size:properties.contentLength}" --output table

That syntax is from the Azure CLI, and specifically it uses JMESPath (a JSON query language) to filter and reshape JSON output from Azure commands.

Let’s break it down:

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The context

Most az CLI commands return JSON objects. For example, listing blobs:

az storage blob list \
  --account-name mystorage \
  --container-name mycontainer \
  --output json

Might return something like:

[
  {
    "name": "file1.txt",
    "properties": {
      "contentLength": 1234,
      "contentType": "text/plain"
    }
  },
  {
    "name": "file2.csv",
    "properties": {
      "contentLength": 5678,
      "contentType": "text/csv"
    }
  }
]

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The query explained

--query "[].{Name:name, Size:properties.contentLength}"

1. []

2. Means "go through each item in the JSON array".

3. {Name:name, Size:properties.contentLength}

4. Reshapes each object to keep only:

   • Name → mapped from the field name.
   • Size → mapped from nested field properties.contentLength.

So the output would look like:

[
  {
    "Name": "file1.txt",
    "Size": 1234
  },
  {
    "Name": "file2.csv",
    "Size": 5678
  }
]

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Why it’s useful

• Avoids huge JSON output.
• Lets you extract just the fields you care about.
• Works with --output table for nice tabular summaries:

az storage blob list \
  --account-name mystorage \
  --container-name mycontainer \
  --query "[].{Name:name, Size:properties.contentLength}" \
  --output table

Gives:

Name       Size
---------  -----
file1.txt  1234
file2.csv  5678

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