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To become an Azure Data Engineer in 2025, you need expertise in:
– 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 2 end-to-end projects
- Prepare an ATS-compliant resume
- Focus on interview preparation
For hands-on learning, consider joining the 90 Days Live Program by Srinivas Reddy:
🔗 Register Here
🔗 Check Course Content
You Should Know:
1. Essential SQL Commands for Data Engineering
-- Create a table CREATE TABLE Employees ( ID INT PRIMARY KEY, Name VARCHAR(100), Salary DECIMAL(10,2) ); -- Insert data INSERT INTO Employees VALUES (1, 'John Doe', 75000.00); -- Query data SELECT FROM Employees WHERE Salary > 50000; -- Join tables SELECT e.Name, d.DepartmentName FROM Employees e JOIN Departments d ON e.DeptID = d.DeptID;
2. Python for Data Processing
import pandas as pd
Read CSV
df = pd.read_csv('data.csv')
Data transformation
df['Salary'] = df['Salary'] 1.10 10% raise
Save to Parquet
df.to_parquet('data.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)
Filter and group
filtered_df = df.filter(df["Salary"] > 50000)
grouped_df = df.groupBy("Department").avg("Salary")
4. Azure Data Factory (ADF) CLI Commands
List pipelines az datafactory pipeline list --factory-name "YourFactory" --resource-group "YourRG" Trigger a pipeline run az datafactory pipeline create-run --factory-name "YourFactory" --resource-group "YourRG" --name "YourPipeline"
5. Azure Databricks Automation
Export notebook databricks workspace export_dir /Users/yourname /backup/ --format DBC Run a job via CLI databricks jobs run-now --job-id 123
6. Azure Synapse Analytics
-- Create external table CREATE EXTERNAL TABLE Sales ( OrderID INT, Amount DECIMAL(10,2) ) WITH ( LOCATION = 'sales/', DATA_SOURCE = AzureDataLakeStore );
7. Azure Data Lake Storage (ADLS) Commands
Upload file to ADLS az storage blob upload --account-name "YourStorage" --container "data" --file "local.csv" --name "remote.csv" List files az storage blob list --account-name "YourStorage" --container "data"
8. Azure Key Vault Secrets Management
Retrieve a secret az keyvault secret show --vault-name "YourVault" --name "DbPassword" Set a secret az keyvault secret set --vault-name "YourVault" --name "ApiKey" --value "12345"
What Undercode Say:
Mastering Azure Data Engineering requires hands-on practice with real-world datasets. Use Linux commands (grep, awk, sed) for log analysis, PowerShell for Azure automation, and Docker for containerized ETL workflows.
🔹 Linux Log Analysis:
grep "ERROR" /var/log/syslog | awk '{print $6}' | sort | uniq -c
🔹 Windows PowerShell for Azure:
Get-AzResourceGroup | Where-Object { $_.Tags["Env"] -eq "Prod" }
🔹 Docker for Data Pipelines:
docker run -v $(pwd)/data:/data python-etl:latest
Expected Output: A structured, high-performance data pipeline that transforms raw data into actionable insights using Azure services.
Further Learning:
🔗 Azure Data Engineering Documentation
🔗 PySpark Official Guide
References:
Reported By: Neha Jain – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
