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Cloud Data Engineering GCP vs AWS vs Azure – Visualpath.pptx
- 1. CLOUD DATA ENGINEERING: www.visualpath.in GCP VSAWS VS AZURE – A BEGINNER’S GUIDE
- 2. INTRODUCTIO N Cloud Data Engineeringis 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
- 3. These platforms offerscalable 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
- 4. WHAT IS ADATA 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, SPARK, DATA PIPELINES ANALYSIS
- 5. WHY CHOOSE CLOUDPLATFORMS? • 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
- 6. Feature GCP AWSAzure 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 www.visualpath.in
- 7. 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
- 8. AWS FOR 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
- 9. 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
- 10. SKILLS NEEDED TOBECOME 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 01 02 03 www.visualpath.in
- 11. TOP CERTIFICATIONS FOR STUDENTS •Google: Associate Cloud Engineer / Professional Data Engineer • AWS: Certified Data Analytics – Speciality • Azure: Microsoft Certified Azure Data Engineer Associate
- 12. CAREER OPPORTUNITIES Cloud DataEngineer Big Data Developer ETL Developer Data Pipeline Architect 01 02 03 04 Salary (India): ₹6L to ₹20L+ depending on experience
- 13. 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)
- 14. CONCLUSION Cloud Data Engineeringis 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
- 15. THANK YOU FORYOUR ATTENTION AND PARTICIPATION. PRESENTATION 2025 +91 7032290546 www.visualpath.in Start building your skills today – the cloud needs YOU!