📘 Full Course Structure Breakdown

🧩 Section 1: Overviews

This section introduces the foundational tools and the overall project flow:

1. Azure Portal
   • Introduction to the Azure platform UI.
   • How to navigate and set up services.
2. Azure Databricks
   • Overview of Databricks on Azure.
   • Setting up the workspace and clusters.
3. Project Overview
   • Real-world use case explanation.
   • Expected outcomes of the data pipeline.
4. Spark Overview
   • Understanding Apache Spark basics.
   • Spark’s role in distributed data processing.

🔧 Section 2: Databricks Fundamentals

Learn how to use Databricks efficiently:

5. Clusters
   • How to create and manage clusters in Databricks.
6. Notebooks
   • Building notebooks using Python/SQL/Scala.
   • Collaboration and code execution.
7. Data Lake Access
   • Connecting Databricks to Azure Data Lake.
8. Securing Access
   • Role-based access controls, tokens, and secrets.
9. Databricks Mounts
   • Mounting external ADLS storage into Databricks.
10. Jobs

• Creating automated jobs and scheduling them.

🐍 Section 3: Spark (Python)

Hands-on with PySpark to build the pipeline:

11. Data Ingestion 1, 2, 3

• Techniques to load raw data from files/APIs.
• Handling different formats (JSON, CSV, Parquet).

12. Transformation

• Cleaning and transforming data using PySpark.

13. Aggregations

• Performing groupBy, count, avg, etc.

14. Incremental Load

• Loading only newly added data efficiently.

🧾 Section 4: Spark (SQL)

Write SQL inside Databricks to manipulate data:

15. Temp Views

• Creating temporary views for querying.

16. DDL (Data Definition Language)

• Creating tables, views, schemas in Spark SQL.

17. DML (Data Manipulation Language)

• Insert, update, delete records in Spark SQL.

18. Analysis

• SQL-based data exploration and reporting.

19. Incremental Load

• SQL version of handling incremental loads.

🌀 Section 5: Delta Lake

20. Delta Lake

• Deep dive into Delta Lake.
• Features like ACID, schema evolution, time travel.

⚙️ Section 6: Orchestration Tools

Make your pipeline production-ready:

21. Azure Data Factory

• Creating and managing pipelines.
• Scheduling data movements and Databricks notebooks.

22. Connecting Other Tools

• Integrating Power BI, Logic Apps, etc.

✅ Final Thoughts

This course is structured to take you from zero to hero in building end-to-end data pipelines on Azure using:

• Databricks
• Spark (Python & SQL)
• Delta Lake
• Azure Data Factory