This repository provides a structured learning path with essential resources for aspiring data engineers. The goal is to help you acquire the necessary skills to become a Junior Data Engineer by covering the core technologies and concepts commonly used in the field.
This roadmap includes the following key areas of data engineering:
Hadoop is a framework for distributed storage and processing of large data sets across clusters of computers. In this section, you'll find curated content to help you understand Hadoop's core components and usage.
Linux is a vital operating system in data engineering. Mastering Linux commands is essential for managing servers, files, and tasks in a data engineering environment.
Git is a version control system, and GitHub is a collaborative platform. Learning these tools is critical for managing code and collaborating with other engineers on data projects.
Python is the go-to language for data engineers due to its simplicity and powerful libraries for data manipulation and analysis. This section offers a selection of resources to help you master Python programming.
Structured Query Language (SQL) is the backbone of data manipulation. It’s a crucial skill for querying databases, building ETL pipelines, and analyzing data.
Docker is essential for creating isolated, reproducible environments. Learning Docker enables you to containerize applications and manage environments efficiently.
Understanding databases, including ACID properties, partitioning, sharding, indexing, and how data is stored, is key to becoming a competent data engineer.
Data warehousing is crucial for managing large datasets and enabling efficient reporting and analysis. Learn about data warehousing concepts, schemas, and modeling techniques in this section.
Apache Airflow is a powerful tool for automating workflows. This section covers the basics of Airflow and how to schedule and monitor data pipelines.
- Start with the Linux section if you’re unfamiliar with operating system commands.
- Move on to Git & GitHub to understand version control and collaboration.
- Follow with Python and SQL to develop your core data engineering skills.
- Dive into more specialized technologies like Hadoop, Docker, and Airflow as you progress.
Each section contains a mix of free and paid resources, from articles to videos, ensuring a well-rounded learning experience.
By following this roadmap, you will gain the fundamental knowledge needed to apply for Junior Data Engineer roles, equipped with both practical and theoretical expertise in the most in-demand technologies.
Good luck on your journey to becoming a Data Engineer! 🚀
