Cloud Data Engineering GCP vs AWS vs Azure – Visualpath
                     CLOUD DATA 
ENGINEERING:
GCP VS AWS 
VS AZURE – A 
BEGINNER’S 
www.visualpath.i
GnUIDE
INTRODUCTIO
NCloud Data Engineering is the backbone 
of modern data-driven companies. It 
involves collecting, transforming, and 
storing large volumes of data using 
cloud platforms like Google Cloud 
(GCP), Amazon Web Services 
(AWS), and Microsoft Azure.
www.visualpath.i
n
These platforms offer scalable tools and services for managing and processing 
data in real-time or batch mode, facilitating the extraction of insights and 
supporting informed decision-making.
This beginner-friendly guide helps students understand:
• What is cloud data engineering?
• Which tools are used in GCP, AWS, and Azure
• What skills are needed
• Career opportunities in this growing field
WHAT IS A DATA 
ENGINEER?
• A Data Engineer designs and builds data pipelines
• They prepare data for analytics and machine learning
• Work involves tools like SQL, Python, Spark, and cloud 
services
• Goal: Turn raw data into clean, usable formats for 
analysis
SQL, PYTHON, DATA PIPELINES ANALYSIS
SPARK,
WHY CHOOSE CLOUD 
P•LFAlexTibleF: EOasRily sMcaleS u?p/down as per data load
• Affordable: Pay only for what you use
• Accessible: Work from anywhere using cloud 
interfaces
• Powerful: Use advanced AI/ML tools and big 
data services
www.visualpath.i
n
Feature GCP AWS Azure
ETL Tool Dataflow, Dataproc AWS Glue, EMR Data Factory, Databricks
Analytics BigQuery Redshift Synapse Analytics
Storage Cloud Storage S3 Azure Blob Storage
ML Tools Vertex AI SageMaker Azure Machine Learning
GCP FOR 
DATA 
ENGINEERIN
• BigQuery: Serverless analytics platform
•GDataflow: Real-time stream/batch processing 
(based on Apache Beam)
• Cloud Composer: Workflow automation using 
Apache Airflow
www.visualpath.i • Best For: Real-time insights, easy to get 
n started
AWS FOR 
DATA 
E• RNedsGhiftI: NScalaEbleE daRta wIarNehousing
G• AWS Glue: Serverless ETL
• S3: Object storage for raw and processed data
• EMR: Big data processing with Spark/Hadoop
• Best For: Enterprise-level data lakes and a 
mature ecosystem
AZURE FOR 
DATA 
E• NAzuGre SIyNnapEse:E CoRmbiIneN bigG data and analytics
• Data Factory: Pipeline building and orchestration
• Azure Databricks: Collaborative analytics and 
machine learning
• Best For: Integration with Microsoft tools like 
Power BI and Excel
SKILLS NEEDED TO 0
BECOME A CLOUD DATA 1
E•NBaGsicIsN ofE cloEuRd computing 0
• SQL and Python
• Data warehousing concepts 2
• Big data frameworks (Spark, Hadoop)
• Tools like Airflow, dbt, Kafka 0
• Understanding of data modelling and 
transformation 3 www.visualpath.i
n
TOP CERTIFICATIONS 
FOR STUDENTS
• Google: Associate Cloud Engineer / Professional Data 
Engineer
• AWS: Certified Data Analytics – Speciality
• Azure: Microsoft Certified Azure Data Engineer Associate
CAREER 
OPPORTUNITIES
0 Cloud Data Engineer
1
0 Big Data Developer
2
03 ETL Developer
04 Data Pipeline Architect
Salary (India): ₹6L to ₹20L+ depending on 
experience
FINAL TIPS FOR 
BEGINNERS
• Pick one cloud and start practising (GCP is beginner-friendly)
• Focus on learning by doing: hands-on labs, projects
• Master SQL and Python
• Stay updated with cloud trends (e.g., Serverless, GenAI)
CONCLUSION
Cloud Data Engineering is an 
exciting career path with growing 
demand across industries.
  Whether you choose GCP, AWS, 
or Azure, mastering cloud 
platforms and data tools will 
unlock great opportunities for you. www.visualpath.i
n
THANK YOU FOR 
YOUR ATTENTION 
AND PARTICIPATION.
PRESENTATION 
2Sta0rt 2bu5ilding your skills today – the cloud needs 
YOU!
+91 7032290546
www.visualpath.in