Roadmaps To Become Azure Data Engineer In

To become an Azure Data Engineer in 2025, you need expertise in the following key areas:

SQL
Python
PySpark
Azure Data Factory
Azure Databricks
Azure Synapse Analytics
Azure Data Lake Storage
Azure Key Vault
Microsoft Fabric

Additionally, you should complete at least two end-to-end projects, prepare an ATS-compliant resume, and undergo interview preparation.

If you’re looking to get into Azure Data Engineering, consider joining the 90 Days Live Program by Srinivas Reddy Sir.

Starting Date: 2nd April 2025 (Wednesday)
Time: 9:00 PM – 9:00 PM IST
First Two Sessions: Free for Demo
Register Here: https://lnkd.in/dAQppJPe
Course Content: https://lnkd.in/dUekK2a4

You Should Know:

1. Essential SQL Commands for Data Engineering

-- Create a table 
CREATE TABLE Employees ( 
EmployeeID INT PRIMARY KEY, 
FirstName VARCHAR(50), 
LastName VARCHAR(50), 
Department VARCHAR(50) 
);

-- Insert data 
INSERT INTO Employees VALUES (1, 'John', 'Doe', 'IT');

-- Query data 
SELECT  FROM Employees WHERE Department = 'IT';

-- Join tables 
SELECT e.FirstName, e.LastName, d.DepartmentName 
FROM Employees e 
JOIN Departments d ON e.DepartmentID = d.DepartmentID;

2. Python for Data Processing

import pandas as pd

Read CSV 
data = pd.read_csv('data.csv')

Data cleaning 
data.dropna(inplace=True)

GroupBy operations 
grouped_data = data.groupby('Category')['Sales'].sum()

Write to Parquet 
data.to_parquet('output.parquet')

3. PySpark for Big Data

from pyspark.sql import SparkSession

Initialize Spark 
spark = SparkSession.builder.appName("DataProcessing").getOrCreate()

Read data 
df = spark.read.csv("data.csv", header=True)

Transformations 
filtered_df = df.filter(df["Age"] > 30)

Write to Delta Lake 
filtered_df.write.format("delta").save("/mnt/datalake/filtered_data")

4. Azure CLI for Data Engineering

 Login to Azure 
az login

Create a resource group 
az group create --name MyResourceGroup --location eastus

Deploy Azure Data Factory 
az datafactory create --name MyADF --resource-group MyResourceGroup

List storage accounts 
az storage account list --resource-group MyResourceGroup

5. Azure Data Factory Commands

 Trigger a pipeline run 
az datafactory pipeline create-run --resource-group MyResourceGroup --factory-name MyADF --pipeline-name MyPipeline

6. Databricks CLI

 List clusters 
databricks clusters list

Run a notebook job 
databricks jobs run-now --job-id 123 --notebook-params '{"param1": "value1"}'

What Undercode Say:

Becoming an Azure Data Engineer requires hands-on experience with SQL, Python, PySpark, and Azure services. Practice these commands and workflows to master data pipelines, ETL processes, and cloud deployments. Automate deployments using Azure CLI and PowerShell, and ensure security with Azure Key Vault.

Expected Output:

Successfully processed data using PySpark. 
Azure Data Factory pipeline executed. 
Delta Lake storage updated.

References:

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Extra Hub: Undercode MoN
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