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.

🚀 Launch Your Personal Agile Tracker

Track your learning journey with our interactive roadmap tracker! This agile-based tool helps you manage your progress through each skill with complete flexibility.

• ✅ One Sprint Per Skill - No over-subdivision, full flexibility to focus on what matters
• 🔄 Drag & Drop Reordering - Move skills between sprints based on your current priorities
• 📅 Dynamic Dates - Start dates auto-calculate 2-week sprints, fully customizable
• ✏️ Editable Skills - Click to rename any skill to match your personal learning goals
• 💭 Personal Comments - Input field for your own remarks and reflections
• ⭐ Self-Rating System - Track your improvement with before/after learning ratings (1-5 stars)
• 📈 Visual Progress Tracking - Interactive sliders and progress bars for each skill
• 📝 Learning Notes - Track concepts understood and concepts needing help
• 💾 Auto-Save + Manual Backup - Your progress never gets lost, with export options
• 📊 Export to Excel/CSV - Share or backup your progress anytime
• ➕ Add/Remove Sprints - Scale up or down based on your learning needs

• Sprint Planning: Drag skills to reorder based on your current priorities and schedule
• Flexible Timeline: Edit start/end dates to match your personal learning pace
• Sprint Retrospective: Use personal comments to reflect on each learning sprint
• Continuous Improvement: Adjust skills and timelines as you discover your learning style
• User-Controlled: "As a learner, I want to..." - you control your own learning path

This roadmap follows a logical progression from foundational to advanced skills. Each section contains curated resources to help you master the technology:

Foundation Skill - Start Here 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. This is your gateway to all other technologies.

Big Data Foundation Hadoop is a framework for distributed storage and processing of large data sets across clusters of computers. Learn Hadoop's core components (HDFS, MapReduce, YARN) and ecosystem tools.

Containerization & Environment Management Docker is essential for creating isolated, reproducible environments. Learning Docker enables you to containerize applications and manage environments efficiently across different systems.

Version Control & Collaboration Git is a version control system, and GitHub is a collaborative platform. These tools are critical for managing code, collaborating with other engineers, and maintaining project history.

Primary Programming Language Python is the go-to language for data engineers due to its simplicity and powerful libraries for data manipulation and analysis. Master Python programming and data-focused libraries like Pandas and NumPy.

Data Query & Manipulation Structured Query Language (SQL) is the backbone of data manipulation. It's a crucial skill for querying databases, building ETL pipelines, and analyzing data across various database systems.

Data Storage & Management Understanding databases, including ACID properties, partitioning, sharding, indexing, and how data is stored, is key to becoming a competent data engineer who can design efficient data systems.

Analytics & Reporting Systems Data warehousing is crucial for managing large datasets and enabling efficient reporting and analysis. Learn about data warehousing concepts, schemas, dimensional modeling, and ETL design patterns.

Workflow Orchestration & Automation Apache Airflow is a powerful tool for automating workflows and data pipelines. Master DAG creation, scheduling, monitoring, and managing complex data pipeline dependencies.

1. Click the tracker link above to launch your personal agile tracker
2. Drag & drop to reorder sprints based on your priorities (we recommend the sequence above)
3. Edit skill names by clicking on them to match your specific goals
4. Set your timeline - dates auto-calculate for 2-week sprints, but customize as needed
5. Rate yourself before starting each skill (1-5 stars) to track improvement
6. Track progress with sliders as you complete learning materials
7. Add personal comments about your experience, challenges, and breakthroughs
8. Export anytime to CSV/Excel format for sharing or offline tracking
9. Your progress auto-saves in your browser - no accounts needed!

1. Start with Linux if you're unfamiliar with command-line operations
2. Follow the sequence above for optimal skill building (each builds on previous knowledge)
3. Use the tracker to manage your progress through each section's resources
4. Mix theory and practice - each section contains articles, tutorials, and hands-on projects
5. Join communities and engage with other learners for support and networking

• Sprint Planning: Choose 1-2 skills to focus on per sprint (typically 2 weeks)
• Daily Practice: Dedicate consistent time each day to learning and hands-on practice
• Sprint Review: Use the tracker to assess progress and adjust plans
• Sprint Retrospective: Reflect on what worked, what didn't, and how to improve
• Continuous Adaptation: Modify your learning path based on interests, job requirements, or market trends

• Completely Private - your data stays on your device, never shared
• No Accounts Required - just visit the link and start tracking
• Works Offline - continues working after first load
• Cross-Device - export/import to sync between devices
• Unlimited Customization - add skills, modify timelines, personalize completely

• Practical Skills: Hands-on experience with all major data engineering tools
• Theoretical Knowledge: Understanding of data engineering principles and best practices
• Portfolio Projects: Real-world projects using the technologies you've learned
• Progress Documentation: Clear record of your learning journey and skill development
• Job Readiness: Fundamental knowledge needed for Junior Data Engineer roles
• Agile Mindset: Flexible, adaptive approach to continuous learning and improvement

• Junior Data Engineer
• ETL Developer
• Data Pipeline Engineer
• Big Data Engineer (entry-level)
• Database Developer
• Analytics Engineer

📊 Interactive Tracking: Visual progress keeps you motivated and organized
🎯 Agile Methodology: Adapt your learning path as you discover your interests and strengths
📚 Curated Resources: High-quality, tested learning materials in each technology area
🔄 Flexible Sequence: Follow our recommendations or create your own path
💪 Practical Focus: Emphasis on hands-on skills that employers actually need
🏃‍♂️ Self-Paced: Learn at your own speed with clear milestones and progress tracking

Good luck on your journey to becoming a Data Engineer! 🚀

Ready to start your agile learning journey? Launch your tracker now and take control of your data engineering career path!