Course Content
Module 1: Introduction to Databricks
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1.1 What is Databricks?
1.1.1 Overview
:: History and evolution of Databricks
:: Key features and benefits
1.1.2 Databricks vs. Traditional Data Platforms
:: Comparison with traditional data warehousing and processing tool
:: Advantages of using Databricks for big data and AI/ML
1.2 Databricks Architecture
1.2.1 Components
:: Workspace, Clusters, Notebooks, Jobs
:: Delta Lake and its role in Databricks
1.2.2 Integration with Apache Spark
:: Overview of Apache Spark
:: Databricks Runtime and Spark versions
1.3 Setting Up Databricks
1.3.1 Creating and Configuring an Account
:: Setting up Databricks on AWS, Azure, or GCP
:: Workspace configuration and navigation
1.3.2 Cluster Setup and Management
:: Creating, configuring, and managing clusters
:: Auto-scaling and cluster termination
1.4 Hands-On Exercise: Getting Started with Databricks
1.4.1 Workspace Exploration
:: Navigate the Databricks workspace and interface
1.4.2 Cluster Configuration
:: Set up a basic cluster and explore cluster settings
Module 2: Working with Databricks Notebooks
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2.1 Introduction to Databricks Notebooks
2.1.1 Overview
:: Features and capabilities of Databricks notebooks
:: Collaboration and sharing
2.1.2 Notebook Languages
:: Using Scala, Python, SQL, and R in notebooks
2.2 Writing and Running Code
2.2.1 Creating and Executing Notebooks
:: Writing code in different languages
:: Running cells and managing outputs
2.2.2 Visualizations and Markdow
:: Creating visualizations from data
:: Using Markdown for documentation
2.3 Advanced Notebook Features
2.3.1 Version Control
:: Integration with Git for version control
2.3.2 Notebook Workflows
:: Parameterizing notebooks and creating reusable workflows
2.4 Hands-On Exercise: Building and Using Notebooks
2.4.1 Basic Notebook Tasks
:: Develop and run notebooks using different languages
2.4.2 Advanced Features
:: Implement version control and parameterized workflows
Module 3: Data Processing with Apache Spark
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3.1 Introduction to Apache Spark
3.1.1 Overview and Features
:: Spark architecture and core components
:: RDDs (Resilient Distributed Datasets) and DataFrames
3.1.2 Spark SQL
:: SQL queries and DataFrame API
:: Performance optimization with Spark SQL
3.2 Working with Spark in Databricks
3.2.1 Spark Configurations
:: Setting up and tuning Spark configurations
3.2.2 Data Ingestion and Transformation
:: Reading and writing data in various formats (CSV, JSON, Parquet)
:: Data cleaning and transformation with Spark
3.3 Advanced Spark Techniques
3.3.1 Performance Tuning
:: Optimizing Spark jobs and queries
:: Managing resources and job execution
3.3.2 Streaming with Spark
:: Overview of Spark Streaming
:: Building and managing streaming jobs in Databricks
3.4 Hands-On Exercise: Processing Data with Spark
3.4.1 Data Ingestion and Transformation
:: Implement Spark jobs for data processing tasks
3.4.2 Performance Optimization
:: Optimize Spark jobs for efficiency and performance
Module 4: Delta Lake and Data Management
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4.1 Introduction to Delta Lake
4.1.1 Overview and Benefits
:: Delta Lake architecture and features
:: ACID transactions and schema evolution
4.1.2 Delta Lake vs. Traditional Data Lakes
:: Comparison with other data lake solutions
4.2 Working with Delta Lake
4.2.1 Creating and Managing Delta Tables
:: Writing and querying Delta tables
:: Handling upserts and deletes
4.2.2 Data Versioning and Time Travel
:: Using Delta Lake for data versioning and auditing
:: Time travel queries and rollback
4.3 Advanced Delta Lake Features
4.3.1 Performance Optimization
:: Optimizing Delta tables with Z-Ordering and data skipping
4.3.2 Integration with BI Tools
:: Connecting Delta tables with BI tools for analytics
4.4 Hands-On Exercise: Using Delta Lake
4.4.1 Data Management Tasks
:: Create, manage, and query Delta tables
4.4.2 Optimization and BI Integration
:: Optimize Delta tables and integrate with BI tools
Module 5: Integrating Databricks with Cloud Services
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5.1 Cloud Storage Integration
5.1.1 AWS, Azure, and GCP Integration
:: Connecting Databricks with cloud storage solutions (S3, ADLS, GCS)
5.1.2 Data Loading and Exporting
:: Loading data from cloud storage into Databricks
:: Exporting data from Databricks to cloud storage
5.2 Databricks and Data Warehousing
5.2.1 Integration with Data Warehouses
:: Connecting Databricks to cloud-based data warehouses (Redshift, Synapse, BigQuery)
5.2.2 ETL Processes
:: Implementing ETL pipelines with Databricks
5.3 Security and Compliance
5.3.1 Data Security
:: Managing data security and encryption in Databricks
5.3.2 Compliance
:: Ensuring compliance with data governance and regulations
5.4 Hands-On Exercise: Cloud Integration
5.4.1 Data Ingestion and Export
:: Load data from and export data to cloud storage solutions
5.4.2 ETL Pipeline Development
:: Create ETL pipelines integrating Databricks with cloud services
Module 6: Machine Learning and Advanced Analytics
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6.1 Introduction to MLflow
6.1.1 Overview and Features
:: MLflow components: Tracking, Projects, Models, Registry
6.1.2 Integrating MLflow with Databricks
:: Tracking experiments and managing models
6.2 Building Machine Learning Models
6.2.1 Model Development
:: Using Spark MLlib and Databricks ML runtime
:: Feature engineering and model training
6.2.2 Model Evaluation and Tuning
:: Evaluating model performance and hyperparameter tuning
6.3 Advanced Analytics
6.3.1 Graph Analytics
:: Overview of GraphFrames and graph processing
6.3.2 Deep Learning
:: Integrating deep learning frameworks with Databricks
6.4 Hands-On Exercise: ML and Advanced Analytics
6.4.1 Model Development
:: Develop and train machine learning models using MLflow
6.4.2 Advanced Analytics
:: Implement graph analytics and deep learning tasks
Module 7: Performance Tuning and Best Practices
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7.1 Performance Tuning Techniques
7.1.1 Spark Job Optimization
:: Optimizing Spark jobs and queries for performance
7.1.2 Cluster Management
:: Managing and scaling clusters for optimal performance
7.2 Data Management Best Practices
7.2.1 Efficient Data Handling
:: Best practices for data storage and retrieval
7.2.2 Security and Governance
:: Implementing data security and governance practices
7.3 Monitoring and Troubleshooting
7.3.1 Monitoring Tools
:: Using Databricks monitoring tools for cluster and job management
7.3.2 Troubleshooting
:: Identifying and resolving common issues
7.4 Hands-On Exercise: Tuning and Best Practices
7.4.1 Performance Optimization
:: Optimize and troubleshoot Spark jobs and clusters
7.4.2 Data Management and Security
:: Apply best practices for data management and security
Module 8: Real-World Projects and Case Studies
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8.1 Industry Case Studies
8.1.1 Real-World Applications
:: Case studies from various industries (e.g., finance, healthcare, retail)
8.1.2 Lessons Learned
:: Key takeaways and best practices from real-world implementations
8.2 Project
8.2.1 Project Development
:: Develop a comprehensive Databricks-based solution involving data processing, analytics, and machine learning
8.2.2 Presentation and Review
:: Present the project, receive feedback, and iterate on the solution
8.3 Future Trends and Continued Learning
8.3.1 Emerging Technologies
:: Trends in big data, cloud computing, and AI/ML
8.3.2 Resources for Advanced Learning
:: Additional resources, certifications, and community engagement
Databricks
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