📘 Full Course Structure Breakdown
🧩 Section 1: Overviews
This section introduces the foundational tools and the overall project flow:
- Azure Portal
- Introduction to the Azure platform UI.
- How to navigate and set up services.
- Azure Databricks
- Overview of Databricks on Azure.
- Setting up the workspace and clusters.
- Project Overview
- Real-world use case explanation.
- Expected outcomes of the data pipeline.
- Spark Overview
- Understanding Apache Spark basics.
- Spark’s role in distributed data processing.
🔧 Section 2: Databricks Fundamentals
Learn how to use Databricks efficiently:
- Clusters
- How to create and manage clusters in Databricks.
- Notebooks
- Building notebooks using Python/SQL/Scala.
- Collaboration and code execution.
- Data Lake Access
- Connecting Databricks to Azure Data Lake.
- Securing Access
- Role-based access controls, tokens, and secrets.
- Databricks Mounts
- Mounting external ADLS storage into Databricks.
- Jobs
- Creating automated jobs and scheduling them.
🐍 Section 3: Spark (Python)
Hands-on with PySpark to build the pipeline:
- Data Ingestion 1, 2, 3
- Techniques to load raw data from files/APIs.
- Handling different formats (JSON, CSV, Parquet).
- Transformation
- Cleaning and transforming data using PySpark.
- Aggregations
- Performing groupBy, count, avg, etc.
- Incremental Load
- Loading only newly added data efficiently.
🧾 Section 4: Spark (SQL)
Write SQL inside Databricks to manipulate data:
- Temp Views
- Creating temporary views for querying.
- DDL (Data Definition Language)
- Creating tables, views, schemas in Spark SQL.
- DML (Data Manipulation Language)
- Insert, update, delete records in Spark SQL.
- Analysis
- SQL-based data exploration and reporting.
- Incremental Load
- SQL version of handling incremental loads.
🌀 Section 5: Delta Lake
- Delta Lake
- Deep dive into Delta Lake.
- Features like ACID, schema evolution, time travel.
⚙️ Section 6: Orchestration Tools
Make your pipeline production-ready:
- Azure Data Factory
- Creating and managing pipelines.
- Scheduling data movements and Databricks notebooks.
- 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
