Azure Data Bricks(Mastery) Syllabus

1. Azure Data Factory Fundamentals

• ADF Concepts: Modern Data Engineering Architecture & Azure Data Factory Overview
• Core Components: Linked Services, Datasets, Pipelines & Integration Runtime
• Data Movement: Copy Activity, Dynamic Content & Expressions

2. ADLS Gen2 Integration

• Storage Architecture: Azure Data Lake Storage Gen2 Fundamentals
• File Processing: CSV, JSON, Parquet & Excel Handling
• Data Organization: Folder Structure Design & Dynamic File Management
• Validation Framework: Enterprise File Validation Techniques

3. File-Based Ingestion Frameworks

• File Processing: Single File, Multiple File & Multiple Folder Processing
• ADF Activities: Lookup, ForEach & Get Metadata Activities
• Framework Design: Parameterized Datasets & Metadata-Driven Ingestion

4. Database Connectivity & Ingestion

• Database Integration: Azure SQL, SQL Server, Oracle & PostgreSQL to ADLS
• Data Migration: Snowflake to Azure SQL Pipelines
• Enterprise Frameworks: Multi-Table & Multi-Server Ingestion Frameworks
• Automation: Stored Procedure Activities

5. SQL for Data Engineering

• SQL Fundamentals: Joins, Aggregate Functions, String & Date Functions
• Advanced SQL: Window Functions, CTEs & Recursive CTEs
• Optimization: Duplicate Handling & Interview Scenarios

6. Incremental Load Frameworks

• Loading Strategies: Full Load vs Incremental Load
• Framework Design: Watermark & Last Modified Date Frameworks
• Enterprise Processing: Delta Loads, Control Tables & Multi-Table Processing

7. Scheduling & Trigger Frameworks

• Pipeline Automation: Schedule, Tumbling Window & Event-Based Triggers
• Real-Time Processing: File Arrival & Dependency-Based Triggers
• Best Practices: Trigger Design & Monitoring

8. Enterprise Metadata-Driven Frameworks

• Metadata Design: Metadata Tables & Dynamic Source Processing
• Dynamic Frameworks: Dynamic Sink Processing & Schema Mapping
• Reusable Pipelines: Single Pipeline for Multiple Sources

9. REST API Integration

• API Fundamentals: REST APIs & Authentication Mechanisms
• Security: Bearer Token Authentication
• Data Processing: Pagination, JSON Processing & Error Handling
• Integration Framework: REST API to ADLS Automation Framework

10. Mapping Data Flows

• Transformations: Data Cleansing, Validation & Derived Columns
• Advanced Processing: Aggregates, Joins, Pivot & Unpivot Operations
• Data Warehousing: SCD Type 1 & Type 2 Implementations

11. Security & Automation

• Security: Azure Key Vault Integration & Credential Management
• Automation: Logic Apps Integration & Email Notifications
• Alerting: Pipeline Status Monitoring & Automated Alerts

12. Monitoring & Production Support

• Monitoring Frameworks: Logging, Auditing & Error Tracking
• Performance Optimization: Retry Mechanisms & Concurrency Management
• Production Support: Troubleshooting & Real-Time Support Scenarios

13. Real-Time Industry Projects

• Project 1: SQL Server to ADLS Enterprise Ingestion Framework
• Project 2: Multi-Table Incremental Load Framework
• Project 3: Metadata-Driven Enterprise Pipeline
• Project 4: REST API to ADLS Automation Framework
• Project 5: Event-Based File Processing Solution
• Project 6: End-to-End Azure Data Factory Enterprise Project

14. Interview Preparation Program

• ADF Interview Preparation: 100+ Azure Data Factory Questions
• SQL Interview Preparation: 100+ SQL Questions
• Career Readiness: Mock Interviews & Resume Building Assistance
• Industry Exposure: Production Support Scenarios & Best Practices

15. Course Highlights

• 90+ Hands-On Labs & Enterprise-Level Frameworks
• Real-Time Industry Projects & Metadata-Driven Design Patterns
• Incremental Load Architectures & REST API Integrations
• Production Support Knowledge & Career Guidance
• Lifetime Learning Community Access