Course Overview

Data Engineering & Big Data Tools

Module 1: Foundational Skills: SQL & Python for Data

This foundational module ensures mastery of the two most critical languages in the data engineering domain. We begin with SQL (Structured Query Language), emphasizing advanced querying techniques, including window functions, subqueries, complex joins, and performance optimization (indexing). Concurrently, proficiency in Python is established, focusing on libraries essential for data manipulation: Pandas for in-memory data processing, and libraries for interacting with databases and cloud storage APIs. The goal is to efficiently extract, clean, and pre-process data using industry-standard programmatic and declarative methods.

Module 2: ETL Concepts and Data Warehousing

This module moves beyond basic cleaning to focus on the complete ETL lifecycle (Extract, Transform, Load). Learners will gain a conceptual understanding of Data Warehousing, including schemas like Star and Snowflake models, and the differences between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems. The core of the module covers Transformation (T) logic: data validation, normalization, aggregation, and handling schema evolution. Emphasis is placed on designing robust, scheduled, and reliable ETL pipelines, and understanding the shift towards ELT (Extract, Load, Transform) models favored by cloud data warehouses.

Module 3: Introduction to Hadoop and Distributed Storage

This module introduces the concepts and architecture required for managing Big Data. Learners will be introduced to Apache Hadoop, specifically its core components: the Hadoop Distributed File System (HDFS) for reliable, fault-tolerant storage, and the foundational concept of MapReduce for distributed processing. The focus here is on understanding distributed computing principles: how large datasets are split, processed in parallel across clusters of commodity hardware, and why data locality is critical for performance in Big Data environments.

Module 4: High-Performance Processing with Apache Spark

Building upon the concepts of distributed computing, this module introduces Apache Spark, the modern, high-speed, general-purpose engine for large-scale data processing. The advantage of Spark’s in-memory processing model over Hadoop’s disk-based MapReduce is highlighted. Key concepts covered include the Resilient Distributed Dataset (RDD) and the structured APIs (DataFrames and Datasets). Practical application will focus on using PySpark to write transformation jobs for complex data operations, aggregations, and streaming data analysis.

Module 5: End-to-End Data Pipeline Mini Project

The final module is a capstone project that requires learners to integrate all skills into a functional Data Pipeline. The Mini Project involves defining a source (e.g., a simulated stream or a file in cloud storage), designing the necessary transformations (using Python/Spark), and orchestrating the movement of data into a target system (e.g., a database or data lake). This project emphasizes the practical challenges of data engineering, including scheduling, monitoring, basic error handling, and ensuring data quality throughout the entire lifecycle of the pipeline.