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If you’re preparing for data engineering interviews, mastering these key technologies is crucial. Below are free resources to help you build expertise in SQL, Python, Big Data, Cloud platforms, and DevOps:
1. SQL
2. Python
3. PySpark
4. Hadoop & Spark
https://lnkd.in/gG47vYkK
https://lnkd.in/gSp6_iwA
https://lnkd.in/gRBrMRb4
5. Azure
6. AWS
7. GCP
8. Understanding Pipelines
9. Airflow
10. CI/CD
11. Data Modeling & Warehousing
12. Download FREE Materials
You Should Know:
Essential SQL Commands for Data Engineering
-- Create a table CREATE TABLE employees (id INT, name VARCHAR(100), salary DECIMAL(10,2)); -- Insert data INSERT INTO employees VALUES (1, 'John Doe', 75000.50); -- Query with aggregation SELECT department, AVG(salary) FROM employees GROUP BY department; -- Join tables SELECT e.name, d.department_name FROM employees e JOIN departments d ON e.dept_id = d.id;
Python for Data Processing
Read CSV with Pandas
import pandas as pd
df = pd.read_csv('data.csv')
Data transformation
df['new_column'] = df['old_column'] 2
Write to Parquet (optimized for Big Data)
df.to_parquet('output.parquet')
PySpark for Big Data
from pyspark.sql import SparkSession
Initialize Spark
spark = SparkSession.builder.appName("DataProcessing").getOrCreate()
Read data
df = spark.read.csv("bigdata.csv", header=True)
Perform transformations
df_filtered = df.filter(df["salary"] > 50000)
Write output
df_filtered.write.parquet("filtered_data.parquet")
Airflow DAG Example (Automation)
from airflow import DAG
from airflow.operators.bash import BashOperator
dag = DAG('data_pipeline', schedule_interval='@daily')
task1 = BashOperator(task_id='extract_data', bash_command='python extract.py', dag=dag)
task2 = BashOperator(task_id='transform_data', bash_command='python transform.py', dag=dag)
task1 >> task2
AWS CLI for Cloud Engineers
List S3 buckets aws s3 ls Copy files to S3 aws s3 cp local_file.txt s3://my-bucket/ Launch an EC2 instance aws ec2 run-instances --image-id ami-12345 --instance-type t2.micro
Azure Data Factory Commands
Create a resource group az group create --name myRG --location eastus Deploy a Data Factory az datafactory create --name myADF --resource-group myRG
What Undercode Say:
To excel in data engineering, hands-on practice is key. Use these commands and resources to build real-world pipelines. Automate workflows with Airflow, optimize queries in SQL, and leverage PySpark for distributed computing. Cloud platforms (AWS/Azure/GCP) are essential—master their CLI and SDKs.
Expected Output:
- Structured datasets processed via SQL/PySpark.
- Automated pipelines using Airflow/CI-CD.
- Cloud-deployed data solutions.
Keep learning, keep building! 🚀
References:
Reported By: Ajay026 Dataengineering – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
