Spotify Music Database | Azure ETL Project | Data Engineer Portfolio Project | Part 1

🚀 Dive into the world of data engineering as we embark on a journey to construct a powerful music database using Azure ETL pipeline techniques! In this comprehensive video tutorial, I guide you through the entire process, sharing my hands-on experience gained through self-study.

🔍 Key Learnings:
1️⃣ Databricks Mastery: Learn how to leverage Azure Databricks for efficient data transformation and processing, tackling real-world challenges in the process.
2️⃣ Azure Data Factory Orchestration: Gain insights into orchestrating end-to-end workflows in Azure Data Factory, a crucial skill for managing seamless data pipelines.
3️⃣ SQL Reinforcement: Explore the usage of Azure SQL for data storage, reinforcing your SQL skills in a practical, real-world project.
4️⃣ API Interaction: Uncover the intricacies of working with Spotify's API, including authentication and data extraction, offering valuable insights into API usage.
5️⃣ ETL Processes Understanding: Deepen your comprehension of ETL (Extract, Transform, Load) processes and their significance in the realm of data engineering.

🛠️ Tools and Technologies Used:
• Azure Data Factory
• Azure Databricks
• Azure SQL
• Python scripting
• Spotify API

🌐 Scope and Goals: Explore the end-to-end process of constructing a music database, from extracting artist data to efficient data transformations and loading into Azure SQL. Understand how to visualize and report insights using Power BI.

🤔 Challenges Faced and Solutions: Encounter real-world challenges like authentication with Spotify API, efficient handling of album and track data, and navigating the lack of a bulk track endpoint. Learn adaptive approaches and strategic solutions.
📈 Workflow Automation: Discover the power of automation with triggers, making your pipeline dynamic and responsive to changes in underlying data. Witness the creation of a database triggered by new artist data and weekly updates for song data.
🚀 Future Development: Understand the potential for ongoing improvements and expansions, envisioning user profiles and playlist generation. This project serves as a foundation with limitless possibilities for growth and refinement.
🎓 Conclusion and Key Takeaways: Wrap up the tutorial with insights gained from working extensively with Databricks, Azure Data Factory, SQL, and Spotify's API. Reflect on the significance of ETL processes in data engineering.

#Azure #DataEngineering #ETLPipeline #SpotifyAPI #DataScience #tutorial #PorfolioProject

Resources:

[srivardhan]

Waiting for Tableau Dashboard. Thank You for all the valuable content.
Can I add this in my resume?

[ElvisPlazaGavilanes]

I am literally building a project similar to this. thank you so much!

[ranpoedogawa]

Amazing content ! Congrats and thanks 🙂

[Shubham-po]

"Could you create a comprehensive video tutorial that guides users through the entire ETL (Extract, Transform, Load) process like above ? The tutorial should cover extracting data from an API, utilizing Kafka and Spark for transformation, and concluding with data visualization using either Power BI or Tableau."

[abhishekkumar-qiis]

please make a vedio on interview questions of ETL development.Thanks

[alexochoki]

I was actually thinking about how I can ingest data from Spotify API directly into Azure Storage without manually uploading data manually