Welcome to the Data Engineering Specialization repository!
This repository contains my notes, projects, and hands-on work completed while pursuing the 🔗 Data Engineering Specialization (16-course series).
Data Engineering is the backbone of modern data-driven industries. It involves designing, building, and managing systems that collect, store, and transform raw data into valuable insights for Data Scientists, BI Analysts, and AI Engineers.
- Through this specialization, I mastered up-to-date, practical skills that data engineers use daily to solve real-world problems.
- This program is ACE® recommended—when you complete, you can earn up to 12 college credits.
- ✅ Master the most in-demand skills for entry-level Data Engineers
- ✅ Create, design, and manage relational databases with MySQL, PostgreSQL, and IBM Db2
- ✅ Work with NoSQL & Big Data using MongoDB, Cassandra, Cloudant, Hadoop, and Apache Spark
- ✅ Implement ETL & Data Pipelines with Bash, Airflow, and Kafka
- ✅ Architect, populate, and deploy Data Warehouses and create BI reports/dashboards
- ✅ Learn Generative AI techniques and how they apply to Data Engineering workflows
- 📊 Data Analysis
- 🤖 Generative AI for Data Engineering
- 📥 Data Import/Export
- ⚡ Apache Spark, Spark SQL, ML & Streaming
- 🐍 Python Programming for Data Engineering
- 🗄️ Relational Databases (MySQL, PostgreSQL, IBM Db2)
- 📚 NoSQL (MongoDB, Cassandra, Cloudant)
- ☁️ Data Warehousing
- 🔄 Extract, Transform, Load (ETL)
- 🐧 Linux & Bash Scripting
- 🌐 Web Scraping
Throughout this specialization, I completed hands-on labs and projects, including:
- 🏢 Designing a relational database for a coffee franchise
- 📊 Using SQL to query census, crime, and demographic datasets
- 💻 Writing Bash shell scripts on Linux to automate backups
- 🗃️ Setting up and optimizing data platforms with MySQL, PostgreSQL & IBM Db2
- 🚦 Analyzing road traffic data and creating ETL pipelines with Airflow & Kafka
- 🏭 Designing and implementing a data warehouse for a solid-waste management company
- 📂 Moving, querying, and analyzing data in MongoDB, Cassandra & Cloudant
- 🤖 Training ML models with Apache Spark applications
- 🛠️ Designing, deploying, and managing end-to-end data engineering platforms
- 📘 Notes → Detailed concepts, explanations, and references
- 🛠️ Projects → Real-world data engineering projects with documentation
- 📑 Resources → Links and supporting materials for further practice
Contributions are always welcome! 🚀
If you’d like to improve notes, fix errors, or add new resources:
- Fork the repository
- Create a new branch (
git checkout -b feature-branch) - Commit your changes (
git commit -m "Add feature/update") - Push to your branch (
git push origin feature-branch) - Open a Pull Request 🎉
This repository is licensed under the Creative Commons License (CC BY-NC 4.0).
You are free to use and share the work with proper attribution, but commercial use is not allowed.
🔗 Read more about the license here
KASHIF MAQBOOL JOIYA
🎓 Data Analyst & Data Scientist | Aspiring AI Engineer
💻 Passionate about Open Source, Data Science, Big Data, and AI Systems
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