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CLOUD DATA ENGINEERING: GCPVSAWSVS AZURE – A BEGINNER’S GUIDE www.visualpath.in
INTRODUCTION Cloud 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.in
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 www.visualpath.in
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 ANALYSIS SQL, PYTHON, SPARK, DATA PIPELINES
WHY CHOOSE CLOUD PLATFORMS? • Flexible: Easily scale up/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
GCP FOR DATA ENGINEERING • BigQuery: Serverless analytics platform • Dataflow: Real-time stream/batch processing (based on Apache Beam) • Cloud Composer: Workflow automation using Apache Airflow • Best For: Real-time insights, easy to get started www.visualpath.in
AWSFOR DATA ENGINEERING • Redshift: Scalable data warehousing • 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 ENGINEERING • Azure Synapse: Combine big 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
01 SKILLS NEEDED TO BECOME A CLOUD DATA ENGINEER • Basics of cloud computing • SQL and Python • Data warehousing concepts • Big data frameworks (Spark, Hadoop) • Tools like Airflow, dbt, Kafka • Understanding of data modelling and transformation 02 03 www.visualpath.in
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 Cloud Data Engineer 01 Big Data Developer 02 ETL Developer 03 Data Pipeline Architect 04 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.in
THANK YOU FOR YOUR ATTENTION AND PARTICIPATION. PRESENTATION 2025 Start building your skills today – the cloud needs YOU! +91 7032290546 www.visualpath.in