Mastering Databricks: From Data Engineering to Machine Learning

Mastering Databricks: From Data Engineering to Machine Learning

Chapters:

1. Introduction to Databricks and the Lakehouse Architecture
   • Overview of Databricks
   • Lakehouse architecture and its benefits
   • Key components of Databricks
2. Setting Up Your Databricks Environment
   • Creating a Databricks account
   • Workspace and clusters setup
   • Understanding Databricks pricing and tiers
3. Understanding the Databricks Workspace
   • Navigating the UI
   • Using notebooks and dashboards
   • Collaboration features in Databricks
4. Working with Apache Spark in Databricks
   • Introduction to Apache Spark
   • Using Spark for big data processing
   • Optimizing Spark jobs in Databricks
5. Data Ingestion and ETL with Databricks
   • Connecting to data sources (cloud storage, databases)
   • ETL processes with Databricks Delta
   • Managing structured and unstructured data
6. Databricks Delta Lake
   • Introduction to Delta Lake
   • Handling big data using Delta Lake
   • Implementing version control for datasets
7. Data Engineering with Databricks
   • Designing data pipelines
   • Data transformations with PySpark and SQL
   • Scheduling and automating ETL jobs
8. Data Exploration and Visualization in Databricks
   • Exploratory data analysis (EDA)
   • Using built-in visualization tools
   • Integrating third-party visualization tools (e.g., Tableau, Power BI)

9. Machine Learning with Databricks
   • Introduction to machine learning in Databricks
   • Building ML models using MLlib and scikit-learn
   • Model experimentation and tuning
10. Deep Learning with Databricks

• Using TensorFlow and Keras on Databricks
• GPU acceleration and model training
• Implementing deep learning pipelines

11. Databricks AutoML

• Overview of AutoML in Databricks
• Automatically building and optimizing models
• Analyzing and deploying AutoML results

12. Collaborative Machine Learning with Databricks

• Using Databricks MLflow for tracking experiments
• Model versioning and management
• Collaborative model development and deployment

13. Databricks for Streaming Data Processing

• Real-time data processing with Apache Spark Streaming
• Handling streaming data with Delta Lake
• Use cases for real-time analytics

14. Data Governance and Security in Databricks

• Security features and best practices
• Data governance with Unity Catalog
• Compliance with regulations (e.g., GDPR, HIPAA)

15. Advanced Databricks Features and Best Practices

• Performance optimization techniques
• Best practices for scaling and managing Databricks clusters
• Future trends in Databricks and cloud-based data platforms

This structure covers both foundational and advanced concepts to help users get the most out of Databricks for data engineering, machine learning, and more.