Fundamentals of Data Engineering: Principles and Techniques for Building Scalable Data Pipelines

This course is suitable for anyone who is interested in learning about the principles and techniques of data engineering. This course is for anyone who is interested in a career in data engineering or wants to pursue further education in this field particularly for Data engineers, Data analysts, Data scientists, IT professionals, and Business analysts who want to gain a better understanding of the technical aspects of data processing and storage. The course is for both beginners and professionals who have some knowledge of programming and databases. The course is also beneficial for individuals who are looking to upskill or reskill in the field of data engineering.

By the end of the course, the learners will have a strong understanding of the principles and techniques used in data engineering and will be able to design, implement, and manage data systems that can efficiently handle large volumes of data. Students will also be able to apply the knowledge gained throughout the course to design and implement a scalable and reliable data pipeline as part of a  project.

To get the most out of the course, the learners should have some prior knowledge of the following:

• Basic programming concepts, such as variables, functions, and control structures.

• Basic database concepts, such as data modeling, SQL queries, and database management.

• Basic knowledge of statistics and mathematics.

While these prerequisites are not mandatory, having a basic understanding of these concepts will help students to follow the course content more easily and complete the exercises and projects more effectively. Additionally, the course is designed to be accessible to students from a variety of backgrounds and skill levels, and instructors will be available to provide additional support as needed.

1. Introduction to Data Engineering

• What is Data Engineering?
• The role of Data Engineering in Data-Driven Decision Making
• Key Concepts and Terminology

2. Python Basics

• Variables, Data Types,
• Loops and control structure
• Functions and Classes 
• Arrays and Dictionaries

3. Data Ingestion

• Data Ingestion Process and Techniques
• Batch and Stream Processing
• Data Ingestion Tools

4. Data Storage

• Relational Databases
• SQL Database
• NoSQL Databases
• Big Data Solutions
• Data Warehouses

5. Data Processing

• Data Cleaning
• Data Transformation
• Data Aggregation
• Data Processing Tools

6. Big Data Technologies

• Introduction to Hadoop
• Introduction to Spark
• Introduction to Kafka
• Big Data Processing and Analysis

7. Data Quality and Governance

• Data Quality
• Data Governance Policies
• Data Security, Privacy, and Compliance