This beginner-friendly course is designed for anyone who wants to dive into the world of data engineering. Data engineers are responsible for collecting, organizing, and managing large volumes of data to make it accessible for analysis and business use. If you're new to data engineering, this course will introduce you to the foundational concepts, tools, and technologies needed to build data pipelines from scratch.

By the end of this course, you'll understand the key components of data engineering, including data ingestion, processing, storage, and automation. Through hands-on projects and real-world examples, you’ll learn how to create data pipelines that move data efficiently across systems.

Key Topics:

1. Introduction to Data Engineering: Understanding the role of data engineers and the data lifecycle.
2. Data Types and Structures: Basics of structured, unstructured, and semi-structured data.
3. Databases 101: Introduction to SQL and NoSQL databases, and how to store and retrieve data.
4. Introduction to ETL Pipelines: What are ETL (Extract, Transform, Load) pipelines, and why are they important?
5. Data Ingestion Techniques: Collecting and importing data from multiple sources using APIs and other tools.
6. Data Processing Tools: Overview of popular tools like Python and Pandas for data processing.
7. Introduction to Cloud Storage: Basics of cloud platforms (AWS, Azure, Google Cloud) and their role in data storage.
8. Introduction to Data Warehousing: Simplifying data storage and retrieval with warehouses like AWS Redshift.
9. Data Pipeline Automation: Basics of automating workflows with tools like Apache Airflow.
10. Data Security Basics: How to ensure data privacy and protect sensitive information.
11. Hands-on Project: Building a simple data pipeline from data collection to data storage.