Learn Data Engineering Course Syllabus

Overview: This comprehensive course introduces learners to the full data engineering lifecycle, covering ingestion, transformation, orchestration, storage, and processing with modern tools. Through hands-on projects and real-world scenarios, you’ll gain practical experience building end-to-end data pipelines. The course spans approximately 17 hours of content, divided into seven modules, culminating in a capstone project that simulates industry workflows. Ideal for developers or analysts transitioning into data roles, it blends foundational theory with tool-specific skills used by leading tech companies.

Module 1: Introduction to Data Engineering

Estimated time: 1.5 hours

• What is data engineering
• Role of data engineers in the data team
• Overview of the data engineering lifecycle
• Components of a modern data stack

Module 2: Ingestion Layer

Estimated time: 2.5 hours

• Batch vs. streaming ingestion
• Kafka basics and use cases
• Working with file sources
• Integrating API-based data sources

Module 3: Transformation Layer

Estimated time: 2.5 hours

• Data cleaning techniques
• Data enrichment strategies
• ETL vs. ELT workflows
• Using SQL and Python for transformations

Module 4: Orchestration with Airflow

Estimated time: 2 hours

• Understanding DAGs (Directed Acyclic Graphs)
• Task scheduling and dependencies
• Monitoring and error handling
• Setting up retries and alerts

Module 5: Storage and Warehousing

Estimated time: 2 hours

• Columnar vs. row-based storage formats
• Data warehouse fundamentals
• Introduction to Snowflake
• Loading and querying data in Snowflake

Module 6: Processing with Spark

Estimated time: 3 hours

• Spark architecture and components
• RDDs vs. DataFrames
• Parallel processing concepts
• Processing large datasets using PySpark

Module 7: Real-World Project: End-to-End Pipeline

Estimated time: 3.5 hours

• Designing a complete data pipeline
• Integrating ingestion, transformation, and orchestration
• Storing and querying in a data warehouse

Prerequisites

• Familiarity with SQL
• Basic knowledge of Python
• Understanding of command-line interfaces

What You'll Be Able to Do After

• Understand the full data engineering lifecycle from ingestion to analytics
• Work with key tools like Kafka, Airflow, Spark, and Snowflake
• Design and build data pipelines using both batch and streaming methods
• Handle data transformation, warehousing, and orchestration in real-world scenarios
• Build foundational skills for modern data stacks and cloud-based workflows