Complete Data Engineering Architecture | Hadoop vs Azure vs AWS | Full Tech Stack Explained
Complete Data Engineering Architecture Explained | Hadoop vs Azure vs AWS
Want to understand how a Data Engineering architecture works across On-Premise, Microsoft Azure, and AWS Cloud?
In this video, I explain the complete Data Engineering tech stack used by top companies. You'll learn how different tools fit into each stage of the data pipeline—from data ingestion to storage, processing, and serving.
Whether you're preparing for Data Engineering interviews or learning cloud technologies, this video provides a practical comparison between Hadoop, Azure, and AWS.
📚 What You'll Learn
✅ On-Premise (Hadoop) Architecture
✅ Azure Data Engineering Architecture
✅ AWS Data Engineering Architecture
✅ Data Sources
✅ Data Ingestion Layer
✅ Data Storage Layer
✅ Data Processing Layer
✅ Serving Layer
✅ Hadoop vs Azure vs AWS Comparison
✅ Real-World Data Engineering Stack
✅ Which tools are used by companies?
☁️ Technologies Covered
🟢 On-Premise (Hadoop)
✔ MySQL
✔ Sqoop
✔ HDFS
✔ Apache Spark
✔ MapReduce
✔ HBase
🔵 Microsoft Azure
✔ Azure Data Factory (ADF)
✔ Azure Data Lake Storage Gen2 (ADLS Gen2)
✔ Azure Databricks
✔ Azure Synapse Analytics
✔ Azure SQL Database
✔ Azure Cosmos DB
🟠 Amazon Web Services (AWS)
✔ AWS Glue
✔ Amazon S3
✔ Amazon Athena
✔ Amazon Redshift
✔ Amazon RDS
✔ DynamoDB
🎯 This Video Is Perfect For
✔ Data Engineering Beginners
✔ Azure Data Engineers
✔ AWS Cloud Engineers
✔ Hadoop Developers
✔ Big Data Engineers
✔ Databricks Developers
✔ PySpark Developers
✔ SQL Developers
✔ College Students
✔ Interview Preparation
📈 Why This Video Matters
Understanding the complete Data Engineering architecture helps you:
Design scalable data pipelines
Understand cloud migration
Choose the right tools for each stage
Prepare confidently for Data Engineering interviews
Build production-ready data solutions
📌 Subscribe for more tutorials on:
SQL
Python
PySpark
Azure Data Factory
Azure Databricks
Snowflake
Delta Lake
Apache Spark
Azure Synapse
AWS Data Engineering
Interview Preparation
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