Introduction to Lakehouse

• Concept and Evolution: Understanding the evolution from Data Lakes and Data Warehouses to Lakehouse.
• Lakehouse vs. Traditional Systems: Comparing features and capabilities with traditional data systems.

Architecture of Databricks Lakehouse

• Core Components: Detailed overview of the architecture, including Delta Lake, Photon, and Unity Catalog.
• Lakehouse Platform Workloads: Exploring different workloads like data warehousing, data engineering, data streaming, etc.

Security in Lakehouse

• Security Fundamentals: Discussing the security features within the Databricks Lakehouse platform.
• Best Practices: Strategies for maintaining secure and compliant data environments.

Lakehouse in Action

• Real-world Applications: Case studies demonstrating the practical use of the Lakehouse platform.
• Workload Management: Techniques for effectively managing diverse workloads on the platform.

Understanding Unity Catalog

• Key Features: Exploration of Unity Catalog’s capabilities and how it acts as a central hub for data.
• Integration with Databricks: How Unity Catalog integrates and enhances the Databricks environment.

Administration and Security

• Managing Access: Setting up and managing user and group access.
• Auditing and Compliance: Tools and strategies for auditing and ensuring compliance.

Hands-on With Unity Catalog

• Practical Exercises: Working with the Unity Catalog for data governance and management.
• Best Practices: Tips for efficient use of Unity Catalog in various scenarios.

Fundamentals of Data Engineering

• ETL Processes: Deep dive into Extract, Transform, Load processes in Databricks.
• Data Modeling and Design: Principles of effective data modeling and design in a Lakehouse environment.

Advanced Data Engineering Techniques

• Data Transformation and Storage: Advanced methods for data transformation and optimal storage.
• Pipeline Orchestration: Strategies for creating and managing robust data pipelines.

Data Engineering Projects

• Hands-On Projects: Real-life project scenarios for practical experience.
• Optimization and Troubleshooting: Techniques for optimizing data pipelines and troubleshooting common issues.

Introduction to Data Science and ML in Databricks

• Key Concepts: Overview of data science and machine learning principles in the context of Databricks.
• Toolsets and Languages: In-depth look at the tools (e.g., Spark ML, MLflow) and languages (Python, Scala) used.

Building and Deploying ML Models

• Model Development: Techniques for developing, training, and tuning machine learning models.
• Deployment Strategies: Approaches for deploying models in various environments including batch and real-time.

Advanced ML Techniques

• Deep Learning and AI: Exploring advanced techniques in machine learning and artificial intelligence.
• Real-World ML Applications: Case studies and practical examples of ML applications in industry.

MLOps and Lifecycle Management

• MLflow for Experimentation: Using MLflow for tracking experiments, model versioning.
• MLOps Best Practices: Strategies for implementing machine learning operations effectively.