Data Engineering: Databases, ETL, Airflow, Warehousing & Big Data

Welcome to Data Engineering Bootcamp: From Fundamentals to Deployment—a career-focused, hands-on program built to help you move from “basic SQL/Python” to confidently building production-grade data pipelines that collect, clean, store, and deliver reliable data for real-world use.

This curriculum is structured as a step-by-step learning journey, so you don’t just learn concepts—you learn how data engineering works in practice: working with different data types, designing ETL/ELT pipelines, orchestrating workflows with Apache Airflow, loading data into data warehouses, and deploying solutions on cloud platforms. Whether you’re transitioning from data analysis, upskilling as a junior data professional, or starting fresh with a basic foundation, you’ll gain a clear framework and practical experience using the same tools and workflows modern data teams rely on.

What You’ll Learn

Module 1: Foundations of Data Engineering
Start with the basics and get clear on what data engineering really involves. You’ll understand the role of a data engineer, how data engineering differs from data science and analytics, and the key skills and tools you’ll use throughout the course.

Module 2: Data & Databases (How Data Is Stored and Organized)
You’ll learn the different types of data (structured, semi-structured, unstructured), how transactional systems differ from analytical systems (OLTP vs OLAP), and how to work with both relational and NoSQL databases—so you can choose the right storage for the job.

Module 3: SQL for Data Engineering
This is where your SQL becomes “data engineer-level.” You’ll move from basic queries to advanced techniques like joins, subqueries, window functions, and optimization. You’ll also learn practical database features like views, indexing, and stored procedures—skills that matter when building pipelines that must scale.

Module 4: Python for Data Engineers
You’ll learn Python specifically for data pipeline tasks: cleaning and transforming data with Pandas, working with APIs and JSON, handling files (CSV, Excel, Parquet), and building in validation and error handling so your pipelines don’t break quietly.

Module 5: ETL/ELT Pipeline Design
Here you’ll learn how to extract, transform, and load data properly—plus how to design scalable pipelines, handle bad data, build logging, and understand the difference between batch and streaming workflows.

Module 6: Orchestration with Apache Airflow
You’ll learn how to automate and manage pipelines using DAGs, scheduling, monitoring, and integrating tasks across Python, SQL, and Bash—so your data workflows run reliably like real production systems.

Module 7: Data Warehousing
You’ll learn how warehouses work, how to model data for analytics (star and snowflake schemas), and how to load data into platforms like BigQuery, Snowflake, or Redshift—so teams can run fast queries and generate insights.

Module 8: Cloud Data Engineering Basics
You’ll understand cloud storage (like S3/GCS), cloud databases, and how modern serverless ETL works (e.g., AWS Glue / Google Dataflow)—plus the key ideas behind deploying pipelines in cloud environments.

Module 9: Data Lakes & Big Data Tools
You’ll explore data lakes, distributed systems (HDFS), and get introduced to Spark/PySpark—so you understand how data engineering scales when data becomes too large for traditional tools.

Module 10: Capstone Project (End-to-End Build)
You’ll design and build a complete ETL pipeline using SQL, Python, and Airflow, with an option to deploy to the cloud. You’ll finish by presenting business-ready insights from the transformed data—so you leave with real proof of skill.

Who This Course Is For

• Aspiring data engineers who want a clear, practical path into the role


• Junior data analysts/scientists transitioning into data engineering


• Anyone with basic SQL/Python knowledge who wants to learn pipeline building and deployment

What You’ll Walk Away With

By the end, you’ll have the skills and confidence to build and manage real data pipelines—plus practical deliverables like ETL workflows, Airflow DAGs, cleaned datasets, warehouse-ready models, cloud-ready pipeline structure, and a capstone project you can showcase. You’ll also gain access to 60+ videos, real-world datasets, hands-on assignments, downloadable resources, and a certification of completion—so you’re not just learning, you’re becoming job-ready.

Key Questions

What are the course requirements?

Access to a Computer or Laptop.

Will the certificate be issued?

Yes, a certificate of completion will be issued at the end of the course at No charge.