Azure Databricks Beginner to Expert is a comprehensive series of lessons designed to take you from your very first Azure Databricks workspace to building enterprise-scale Lakehouse, Data Engineering, AI, and Machine Learning solutions.
Through hands-on examples, real-world scenarios, and best practices, you’ll learn Apache Spark, Delta Lake, Unity Catalog, streaming, performance tuning, DevOps, governance, and modern data architecture building the skills needed to confidently design and operate production-ready data platforms.
Lesson 1 – Introduction to Azure Databricks: Understanding the Modern Lakehouse Platform
Lesson 2 – Why Azure Databricks? Solving Modern Data Engineering and AI Challenges
Lesson 3 – Azure Databricks Architecture Explained: Workspaces, Compute, Storage and Unity Catalog
Lesson 4 – Understanding the Databricks Lakehouse Architecture
Lesson 5 – Azure Databricks vs Snowflake vs Microsoft Fabric: When Should You Choose Each?
Lesson 6 – Creating Your First Azure Databricks Workspace from Scratch
Lesson 7 – Navigating the Azure Databricks User Interface Like a Professional
Lesson 8 – Understanding Databricks Pricing, Compute Costs and Cost Optimization Basics
Lesson 9 – Understanding Databricks Compute: Clusters, SQL Warehouses and Serverless
Lesson 10 – Creating and Managing Compute Clusters Efficiently
Lesson 11 – Creating Your First Notebook in Azure Databricks
Lesson 12 – Understanding Notebook Languages: Python, SQL, Scala and R
Lesson 13 – Using Magic Commands (%sql, %python, %scala and %run)
Lesson 14 – Organizing Large Notebook Projects Using Modular Design
Lesson 15 – Notebook Widgets for Interactive Parameterized Development
Lesson 16 – Passing Parameters Between Notebooks
Lesson 17 – Creating Reusable Notebook Utilities
Lesson 18 – Best Practices for Writing Maintainable Databricks Notebooks
Lesson 19 – Connecting Azure Databricks to Azure Data Lake Storage Gen2
Lesson 20 – Understanding DBFS and Workspace Files
Lesson 21 – Working with Mount Points and Why Unity Catalog Changes Everything
Lesson 22 – Secure Authentication Using Managed Identity and Service Principals
Lesson 23 – Reading CSV, JSON, Excel and Parquet Files Efficiently
Lesson 24 – Understanding File Formats for Analytics: CSV vs JSON vs Avro vs Parquet vs Delta
Lesson 25 – Understanding Apache Spark Architecture for Beginners
Lesson 26 – How Spark Executes Jobs Behind the Scenes
Lesson 27 – Understanding Executors, Drivers and Cluster Managers
Lesson 28 – Lazy Evaluation Explained with Practical Examples
Lesson 29 – Transformations vs Actions in Apache Spark
Lesson 30 – Understanding Partitions and Parallel Processing
Lesson 31 – Narrow vs Wide Transformations Explained
Lesson 32 – Spark DAGs and Execution Plans Simplified
Lesson 33 – Introduction to Spark DataFrames
Lesson 34 – Reading and Writing DataFrames Efficiently
Lesson 35 – Selecting, Filtering and Transforming Data
Lesson 36 – Working with Columns and Expressions
Lesson 37 – Sorting, Grouping and Aggregations
Lesson 38 – Joining Multiple DataFrames Like a Pro
Lesson 39 – Handling Missing Data and Null Values
Lesson 40 – Removing Duplicates and Cleaning Data
Lesson 41 – Working with Dates and Time Functions
Lesson 42 – Window Functions Explained with Real Business Examples
Lesson 43 – Introduction to Spark SQL
Lesson 44 – Creating Temporary Views and Global Views
Lesson 45 – Using SQL Inside Databricks Notebooks
Lesson 46 – Optimizing SQL Queries in Azure Databricks
Lesson 47 – Common Table Expressions (CTEs) and Advanced SQL
Lesson 48 – Introduction to Delta Lake and Why It Matters
Lesson 49 – ACID Transactions Explained in Delta Lake
Lesson 50 – Creating Your First Delta Table
Lesson 51 – Delta Table Versioning and Time Travel
Lesson 52 – Updating and Deleting Data Using MERGE
Lesson 53 – Change Data Capture Using Delta Lake
Lesson 54 – Schema Evolution and Schema Enforcement
Lesson 55 – Vacuum, Optimize and Maintenance Operations
Lesson 56 – Delta Table Constraints and Data Quality
Lesson 57 – Deep Dive into Delta Transaction Logs
Lesson 58 – Building Bronze, Silver and Gold Layers Using the Medallion Architecture
Lesson 59 – Designing Enterprise Data Pipelines
Lesson 60 – Incremental Data Loading Strategies
Lesson 61 – Handling Slowly Changing Dimensions (SCD Type 1 and Type 2)
Lesson 62 – Watermarking and Incremental Processing
Lesson 63 – Error Handling and Dead Letter Queues
Lesson 64 – Data Validation and Data Quality Frameworks
Lesson 65 – Logging and Monitoring Enterprise Pipelines
Lesson 66 – Introduction to Structured Streaming
Lesson 67 – Reading Streaming Data from Event Hubs and Kafka
Lesson 68 – Streaming Data into Delta Lake
Lesson 69 – Trigger Modes and Streaming Checkpoints
Lesson 70 – Watermarking Late Arriving Data
Lesson 71 – Exactly Once Processing Explained
Lesson 72 – Streaming Joins and Stateful Processing
Lesson 73 – Introduction to Delta Live Tables
Lesson 74 – Creating Your First Delta Live Table Pipeline
Lesson 75 – Expectations and Data Quality Rules
Lesson 76 – Incremental Processing Using DLT
Lesson 77 – Pipeline Monitoring and Troubleshooting
Lesson 78 – Introduction to Unity Catalog
Lesson 79 – Catalogs, Schemas and Tables Explained
Lesson 80 – Managing Users, Groups and Permissions
Lesson 81 – Row-Level Security and Column Masking
Lesson 82 – Lineage and Data Discovery
Lesson 83 – External Locations and Storage Credentials
Lesson 84 – Best Practices for Enterprise Governance
Lesson 85 – Introduction to Databricks Workflows
Lesson 86 – Creating Multi-Task Workflows
Lesson 87 – Scheduling and Monitoring Jobs
Lesson 88 – Retry Policies and Failure Handling
Lesson 89 – Parameterizing Enterprise Workflows
Lesson 90 – Introduction to Databricks SQL
Lesson 91 – SQL Warehouses Explained
Lesson 92 – Building Interactive Dashboards
Lesson 93 – Alerts, Queries and Scheduled Reports
Lesson 94 – Performance Tuning Fundamentals
Lesson 95 – Choosing the Right Cluster Size
Lesson 96 – Optimizing Spark Shuffle Operations
Lesson 97 – Broadcast Joins vs Shuffle Joins
Lesson 98 – Adaptive Query Execution (AQE)
Lesson 99 – Z-Ordering Explained
Lesson 100 – Data Skew Detection and Optimization
Lesson 101 – Caching Strategies
Lesson 102 – Partitioning Best Practices
Lesson 103 – File Compaction Strategies
Lesson 104 – Introduction to Machine Learning with Azure Databricks
Lesson 105 – MLflow Fundamentals
Lesson 106 – Experiment Tracking with MLflow
Lesson 107 – Model Registry and Model Lifecycle Management
Lesson 108 – AutoML in Azure Databricks
Lesson 109 – Feature Store Fundamentals
Lesson 110 – Training Distributed Machine Learning Models
Lesson 111 – Hyperparameter Tuning
Lesson 112 – Model Serving
Lesson 113 – Batch vs Real-Time Predictions
Lesson 114 – Git Integration with Azure Databricks
Lesson 115 – Managing Source Control Effectively
Lesson 116 – CI/CD for Azure Databricks
Lesson 117 – Deploying Across Development, Test and Production
Lesson 118 – Infrastructure as Code Using Terraform
Lesson 119 – Databricks Asset Bundles
Lesson 120 – Enterprise Security Best Practices
Lesson 121 – Secrets Management Using Databricks Secret Scopes
Lesson 122 – Private Networking and VNet Injection
Lesson 123 – Managed Identity and Service Principal Authentication
Lesson 124 – Customer Managed Keys (CMK)
Lesson 125 – Encryption, Compliance and Governance
Lesson 126 – Auditing and Monitoring User Activity
Lesson 127 – Monitoring Cluster Performance
Lesson 128 – Understanding Spark UI for Troubleshooting
Lesson 129 – Diagnosing Slow Jobs
Lesson 130 – Monitoring Costs and Usage
Lesson 131 – Workspace Administration Best Practices
Lesson 132 – Designing Enterprise-Scale Lakehouse Architectures
Lesson 133 – Multi-Workspace Architecture Patterns
Lesson 134 – Multi-Tenant Azure Databricks Design
Lesson 135 – Metadata-Driven ETL Frameworks
Lesson 136 – Building Reusable Enterprise Data Engineering Frameworks
Lesson 137 – Data Mesh with Azure Databricks
Lesson 138 – Data Products in Modern Data Platforms
Lesson 139 – Event-Driven Data Engineering Patterns
Lesson 140 – Introduction to Databricks AI and Mosaic AI
Lesson 141 – Building Retrieval-Augmented Generation (RAG) Solutions
Lesson 142 – Vector Search in Azure Databricks
Lesson 143 – Working with Foundation Models
Lesson 144 – AI Agents Using Databricks
Lesson 145 – Prompt Engineering for Enterprise AI Applications
Lesson 146 – Evaluating and Monitoring Generative AI Applications
Lesson 147 – Building an End-to-End Retail Lakehouse Solution
Lesson 148 – Real-Time IoT Analytics Using Azure Databricks
Lesson 149 – Modern Data Warehousing with Delta Lake
Lesson 150 – Financial Data Engineering Best Practices
Lesson 151 – Healthcare Analytics Architecture
Lesson 152 – Customer 360 Data Platform Implementation
Lesson 153 – Building Enterprise Data Pipelines from SAP, SQL Server and APIs
Lesson 154 – Data Migration Strategies into Azure Databricks
Lesson 155 – Enterprise Architecture Patterns for Azure Databricks
Lesson 156 – Lakehouse Design Anti-Patterns and How to Avoid Them
Lesson 157 – Disaster Recovery and Business Continuity Planning
Lesson 158 – Cost Optimization Strategies for Large Azure Databricks Deployments
Lesson 159 – Scaling Azure Databricks for Petabyte-Scale Data Processing
Lesson 160 – Preparing for the Databricks Certified Data Engineer Professional Certification