Azure Data Bricks(Beginner) Syllabus

1. Azure Databricks Fundamentals

• Platform Overview: Azure Databricks Introduction, Modern Data Engineering Architecture
• Workspace & Compute: Workspace Navigation, Cluster Types, Compute Resources
• Best Practices: Runtime Versions, Community Edition Setup & Workspace Best Practices

2. Notebooks & Development Environment

• Notebook Development: Creating, Managing & Executing Notebooks
• Workflow Automation: Notebook Workflows & Scheduling
• Advanced Features: Parameterized Notebooks, dbutils Widgets & Reusable Frameworks

3. Security & Secret Management

• Security Fundamentals: Databricks Security Overview
• Secret Management: Secret Scopes & Secure Credential Storage
• Azure Integration: Azure Key Vault Integration & Best Practices

4. Unity Catalog & Data Governance

• Governance: Unity Catalog, Catalogs, Schemas & Tables
• Storage Management: Managed vs External Tables
• Enterprise Security: ADLS Gen2 Access, Permissions & Governance Best Practices
• Architecture: Unity Catalog Migration & Medallion Architecture

5. Python Fundamentals for Data Engineering

• Core Python: Variables, Data Types & Functions
• Data Processing: Strings, Lists, Tuples, Sets & Dictionaries
• Advanced Concepts: Lambda Functions, Map, Filter, Reduce & Error Handling

6. Spark Architecture & Execution

• Spark Fundamentals: Apache Spark Overview & Architecture
• Execution Flow: Driver, Executor, DAG & Parallel Processing
• Monitoring: Spark UI, Jobs, Stages & Task Execution

7. PySpark Core Concepts

• Data Structures: RDD, DataFrame & Dataset Concepts
• Processing: Transformations, Actions & Lazy Evaluation
• Development: Spark Session & DataFrame Creation Methods

8. DataFrame Operations & Transformations

• Data Ingestion: Reading CSV, JSON & Parquet Files
• Data Transformation: Select, Filter, Distinct & OrderBy Operations
• Data Quality: Schema Management & Null Handling
• Advanced Operations: Aggregations, Union & Column Transformations

9. Joins & Data Integration

• Join Operations: Inner, Left, Right, Full, Semi & Anti Joins
• Performance: Broadcast Join Fundamentals
• Database Connectivity: Azure SQL Integration using JDBC
• Data Movement: Reading & Writing Data to Azure SQL

10. Delta Lake Fundamentals

• Modern Data Lakes: Delta Lake Architecture & Table Creation
• Reliability: ACID Transactions & Time Travel
• Schema Management: Schema Enforcement & Evolution
• Optimization: Delta Lake Best Practices

11. Cluster Management & Optimization

• Cluster Administration: Policies, Auto Scaling & Auto Termination
• Performance: Runtime Selection & Monitoring
• Cost Optimization: Efficient Resource Management & Job Scheduling

12. End-to-End ETL Project

• Project Development: Azure SQL to ADLS Gen2 Pipeline
• Data Processing: ADLS to Databricks Transformation using PySpark
• Storage: Delta Table Creation & Validation Framework
• Automation: End-to-End ETL Workflow Automation

13. Advanced PySpark Concepts

• Advanced Development: User Defined Functions (UDF) & Pandas UDFs
• Analytics: Window Functions & Performance Optimization
• Interview Preparation: Real-Time Scenarios & Common Interview Questions

14. Bonus Modules

• Additional Learning: SQL for Data Engineers & Azure Data Lake Storage Basics
• Career Support: Resume Preparation & Mock Interviews
• Practice Sessions: Databricks Interview Questions & PySpark Coding Exercises
• Industry Knowledge: Real-Time Production Support Scenarios

15. Course Highlights

• Beginner Friendly with Hands-On Labs
• Real-Time Industry Projects & ETL Pipelines
• Unity Catalog Governance & Delta Lake Fundamentals
• Azure SQL Integration & PySpark Development
• Interview Preparation & Resume Building Support
• Community Access & Career Guidance