Azure Databricks

Module 1: Introduction to Azure Databricks

• What is Databricks?
• Overview of Databricks as a unified analytics platform
• Comparison with other data processing tools

• Key Components of Databricks:
• Databricks Workspace
• Clusters
• Notebooks
• Repositories
• Delta Lake

• Databricks Use Cases:
• Data ingestion and transformation
• Data warehousing and analytics
• Machine learning
• Real-time data processing

Module 2: Databricks Workspace and Clusters

• Creating a Databricks Workspace:
• Setting up a workspace
• Understanding workspace settings

• Managing Clusters:
• Creating and configuring clusters
• Cluster types (standard, high-performance)
• Scaling clusters
• Cluster lifecycle management

Module 3: Working with Notebooks

• Notebook Basics:
• Creating and editing notebooks
• Understanding notebook cells
• Running cells and executing code

• Notebook Magic Commands:
• Using magic commands for various tasks (e.g., displaying plots, reading files)

• Sharing and Collaborating on Notebooks:
• Sharing notebooks with others
• Collaborating on notebook content

• Data Migration:
• Migrating data from one system to another

Module 4: The Databricks Runtime

• Databricks Runtime Versions:
• Understanding different runtime versions
• Choosing the right runtime for your needs

• Using Python and Scala:
• Writing and executing Python and Scala code in Databricks
• Using libraries and packages

• Accessing External Data Sources:
• Connecting to various data sources (e.g., S3, Azure Blob Storage, databases)

Module 5: Delta Lake

• Introduction to Delta Lake:
• Understanding Delta Lake as a lakehouse platform
• Benefits of Delta Lake (ACID compliance, schema evolution, time travel)

• Creating and Managing Delta Tables:
• Creating Delta tables from existing data
• Managing Delta table schema
• Time travel and versioning

• Delta Live Tables:
• Building data pipelines using Delta Live Tables
• Defining data quality rules

Module 6: Databricks SQL

• Databricks SQL Overview:
• Using SQL to query data in Databricks
• Creating and managing SQL warehouses

• SQL Queries and Commands:
• Writing SQL queries
• Using SQL functions and operators
• Managing SQL objects (tables, views, databases)

Module 7: Databricks Machine Learning

• Machine Learning with Databricks:
• Preparing data for machine learning
• Building and training machine learning models
• Deploying models as REST APIs

• MLflow:
• Using MLflow for model tracking, versioning, and deployment

• AutoML:
• Automating the machine learning process

Module 8: Databricks Integration

• Integrating with Other Azure Services:
• Integrating Databricks with Azure Data Factory, Azure Synapse Analytics, and other Azure services

• Custom Integrations:
• Creating custom integrations using Databricks APIs

Module 9: Databricks projects

• Hands-on Projects:
• Creating and running pipelines
• Building data flows
• Integrating with various data sources and sinks
• Implementing ETL and ELT patterns
• Troubleshooting and optimizing Databricks workflows

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