This course is designed to help learners become job-ready Data Engineers by mastering the tools and workflows used in modern data systems.
You will learn Python for data handling, SQL for query optimization, and Big Data tools like Apache Hadoop, Spark, and Kafka.
The course also covers cloud data engineering using AWS, Azure, and Google Cloud, along with orchestration tools such as Airflow and Docker.
By the end, you will have built complete ETL/ELT pipelines, optimized large datasets, and deployed real-world data workflows.

Learning Outcomes

Core Data Engineering Skills

• Understand modern data engineering concepts

• Build scalable ETL and ELT pipelines

• Work with batch and streaming data

• Understand data modeling and warehousing

• Automate workflows using orchestration tools

Python for Data Engineering

• Python basics & intermediate concepts

• File handling, JSON, CSV processing

• Data transformation scripts

• Working with APIs

• Handling large data efficiently

Advanced SQL & Databases

• SQL CRUD operations

• Joins, subqueries, views

• Window functions & CTEs

• Query optimization

• Indexing & performance tuning

• Relational databases: MySQL, PostgreSQL

• NoSQL: MongoDB, Cassandra

Big Data Tools

• Hadoop ecosystem overview

• HDFS fundamentals

• Apache Spark (RDD, DataFrame, PySpark)

• Spark SQL & MLlib introduction

• Spark optimization

• Apache Kafka for streaming

Cloud Data Engineering

• AWS: S3, Lambda, Redshift, Glue

• Azure: Data Lake, Data Factory, Synapse

• GCP: BigQuery, Dataflow, Pub/Sub

• Storage, compute & integration services

• Data lake vs data warehouse

• Serverless data pipelines

Data Warehousing & Modeling

• Star & Snowflake schemas

• Fact & dimension tables

• OLAP vs OLTP

• Data lake architecture

• Modern Lakehouse (Delta / Iceberg basics)

Orchestration & Automation

• Apache Airflow

• DAGs, operators, scheduling

• Workflow automation

• Monitoring data pipelines

Containers & DevOps Basics

• Docker for data engineering

• Containerizing ETL processes

• Git & GitHub for version control

• CI/CD basics

Project Work

• ETL Pipeline Development

• Batch Data Processing with Spark

• Streaming Pipeline with Kafka

• Data Warehouse Setup

• End-to-End Cloud Data Pipeline (Final Project)