This roadmap is designed to help you master Azure and Databricks for data engineering, providing a structured approach to learning and tracking your progress

1. Overview

This roadmap outlines a step-by-step guide to mastering Data Engineering with a focus on Azure and Databricks, covering fundamental skills, hands-on projects, and advanced concepts.

2. Phases & Milestones

Phase 0: Programming Fundamentals

Objective: Master the core programming skills required for data engineering, including Python, SQL, and Spark.

Milestones:

• [ ] Master Python for data manipulation and automation (Python for Everybody).
• [ ] Learn SQL for querying and manipulating large datasets (LeetCode SQL Problems).
• [ ] Understand Apache Spark basics, focusing on PySpark
• [ ] Gain proficiency in Data Structures, Algorithms, and working with JSON and Parquet files.

Phase 1: Core Data Engineering Concepts

Objective: Develop foundational knowledge of ETL processes, data modeling, and transformations in Azure.

Milestones:

• [ ] Understand ETL pipeline concepts (Data Engineering Cookbook).
• [ ] Perform data transformations using Python and SQL in Databricks (Databricks Transformations Documentation).
• [ ] Learn Star and Snowflake schemas.
• [ ] Implement data validation and quality checks with tools like Great Expectations.
• [ ] Gain familiarity with Delta Lake.

Phase 2: Azure Data Services

Objective: Deepen your knowledge of Azure’s data services.

Milestones:

• [ ] Learn Azure Blob Storage (Blob Storage Documentation).
• [ ] Explore Azure Data Lake (Data Lake Documentation).
• [ ] Set up Azure SQL Database (SQL Database Documentation).
• [ ] Orchestrate ETL pipelines using Azure Data Factory (Data Factory Documentation).
• [ ] Explore Azure Synapse Analytics (Synapse Documentation).

Phase 3: Advanced Databricks & Spark

Objective: Master Apache Spark within Azure Databricks.

Milestones:

• [ ] Advanced Spark DataFrames and PySpark transformations (Spark SQL Documentation).
• [ ] Implement Spark SQL for distributed querying.
• [ ] Understand Spark Streaming (Spark Streaming Documentation).
• [ ] Configure and use Delta Lake for time travel and audit trails.

Phase 4: Building Data Pipelines

Objective: Design scalable and efficient data pipelines.

Milestones:

• [ ] Build ETL pipelines using Azure Data Factory and Databricks.
• [ ] Implement data partitioning and clustering for optimization.
• [ ] Integrate Azure Event Hubs for streaming (Event Hubs Documentation).

Phase 5: Data Governance & Security

Objective: Implement best practices for data governance and security.

Milestones:

• [ ] Use Azure Purview (Purview Documentation).
• [ ] Set up Databricks Unity Catalog (Unity Catalog Documentation).
• [ ] Learn RBAC (Role-Based Access Control Documentation).
• [ ] Monitor data quality with Great Expectations.

Phase 6: Advanced Topics

Objective: Gain expertise in real-time analytics and ML integration.

Milestones:

• [ ] Implement Delta Live Tables.
• [ ] Use AutoML in Databricks.
• [ ] Monitor workflows with Azure Monitor (Azure Monitor Documentation).

3. Projects