Delta Lake Masterclass: Databricks & Spark Mastery

Master Delta Lake with Databricks & Spark: Real-time data pipelines, Unity Catalog, DLT & Interview Prep

What you'll learn

Build and manage Delta Lake tables (managed vs external) with schema enforcement, schema evolution, and table constraints.

Understand the internal architecture of Delta Lake including transaction logs, deletion vectors, Z-ordering, partitioning, and caching.

Apply Delta Lake features such as Time Travel, Restore, Vacuum, Auto-compaction, Change Data Feed, and Delta Cloning.

Perform Insert, Update, Delete, and Merge (Upsert) operations using Delta Lake with audit logging and performance tuning.

Implement Slowly Changing Dimensions (SCD1 & SCD2) with and without Delta Lake in Spark for real-world data warehouse design.

Optimize performance by converting Parquet to Delta, reordering columns, and applying best practices for query performance.

Develop and manage real-time streaming pipelines using Delta Lake with continuous and one-time triggers.

Use Unity Catalog for secure governance, workspace migration, and managing external vs managed tables.

Design and orchestrate end-to-end ETL pipelines in Databricks using Delta Live Tables and Azure Data Factory (ADF).

Prepare for Azure Databricks and Delta Lake interviews with practical questions and real-world use cases.

Requirements

For the best learning experience, it is recommended to first complete my course “Databricks & Spark Masterclass: Real-Time Data Engineering.” This Delta Lake Master Program builds on top of it.

No prior experience with Delta Lake is required — everything is explained step by step.

Basic understanding of SQL and data concepts (tables, queries, joins) will be helpful.

Familiarity with Apache Spark / PySpark is recommended but not mandatory.

Access to an Azure Databricks workspace (free or paid) is useful for hands-on practice.

A computer with an internet connection to follow along with Databricks, Spark, and Delta Lake labs.

Description

Are you ready to master Delta Lake and take your Databricks & Spark skills to the next level?This course is designed for data engineers, architects, and developers who want to build real-time, scalable, and production-ready data pipelines using the most in-demand tools in the industry.Building on top of my first course, Databricks & Spark Masterclass: Real-Time Data Engineering (recommended for foundational learning), this Delta Lake Masterclass focuses exclusively on advanced Delta Lake concepts, performance optimization, governance, orchestration, and interview preparation.What you’ll learn in this course:Create and manage Delta Tables (managed vs external) with schema enforcement, schema evolution, and constraints.Deep dive into Delta Lake architecture, internal working mechanisms, and storage layers.Implement powerful Delta features like Time Travel, Change Data Feed, Cloning, Optimize, Auto-Compaction, Partitioning & Z-Ordering.Perform SCD1 and SCD2 implementations with and without Delta Lake, using merge operations and audit logging.Build real-time streaming pipelines in Databricks with Delta Lake streaming and AutoLoader.Manage governance with Unity Catalog and design scalable pipelines with Delta Live Tables (DLT).Orchestrate Databricks notebooks with Azure Data Factory (ADF) and optimize pipelines for performance.Prepare for Databricks, Spark, PySpark, and Delta Lake interviews with dedicated sessions and coding practice.Why Choose This Course100% hands-on with real Databricks & Delta Lake projectsCovers latest features like Deletion Vectors, Column Encryption, AutoLoader, Unity Catalog, and DLTStructured learning path: From core Delta features to advanced governance and orchestrationIncludes interview-focused coding sessions for PySpark, SQL, and Delta LakeDesigned by a Data Engineering Architect with 20+ years of experienceBy the End of This Course, You Will Be Able ToBuild end-to-end real-time data engineering pipelines with Delta LakeOptimize performance with advanced Delta featuresManage enterprise-grade governance with Unity CatalogConfidently tackle data engineering interviews for Databricks & Spark rolesEnroll now and become a Delta Lake expert—mastering real-time data engineering with Databricks & Spark.

Overview

Section 1: Getting Started with Delta Lake

Lecture 1 Hands-On : Introduction to Delta Lake and Creating Tables in Multiple Ways

Lecture 2 Hands-On: Managed vs External Tables in Delta Lake with Constraints Δ Utils

Section 2: Delta Lake Internal Architecture

Lecture 3 Hands-On : Explained Delta Lake Architecture: Transaction Logs &Storage Layers

Lecture 4 Hands-On: Internal Working Mechanism of Delta Lake with &without Deletion Vector

Section 3: Mastering Delta Lake Core Features (Part 1)

Lecture 5 Hands-On : Schema Enforcement and Schema Evolution in Delta Lake

Lecture 6 Hands-On : Time Travel, Restore, and Vacuum Optimization Explained

Lecture 7 Hands-On : Deep Clone vs Shallow Clone: Backup and Recovery in Delta Lake

Section 4: Mastering Delta Lake Core Features (Part 2)

Lecture 8 Hands-On : Change Data Feed (CDF) in Delta Lake with Structured Streaming

Lecture 9 Hands-On : Optimize Command & Auto-Compaction for Performance Improvement

Lecture 10 Hands-On : Partitioning Delta Tables: Best Practices and Hands-On Implementation

Lecture 11 Hands-On : Z-Ordering in Delta Lake: Real-Time Partitioning & Query Optimization

Section 5: Advanced Delta Lake Features

Lecture 12 Hands-On : Delta Lake Disk Caching And Column-Level Encryption & Decryption

Lecture 13 Hands-On : Performing Insert, Update, Delete, & Merge Operations in Delta Lake

Section 6: Slowly Changing Dimensions (SCD) in Spark & Delta Lake

Lecture 14 Hands-On: Implementing Slowly Changing Dimension (SCD) Type 1 without Delta Lake

Lecture 15 Hands-On : Implementing SCD Type 1 with Delta Lake and Audit Logging

Lecture 16 Hands-On : Implementing SCD Type 2 without Delta Lake and Audit Logging

Lecture 17 Hands-On : Implementing SCD Type 2 with Delta Lake Merge and Audit Logging

Section 7: Delta Lake Performance Optimization

Lecture 18 Hands-On : Reordering Columns in Delta Tables & Converting Parquet to Delta

Section 8: Real-Time Data Streaming with Delta Lake

Lecture 19 Hands-On : Delta Lake Streaming in Databricks: Continuous vs One-Time Triggers

Section 9: Unity Catalog & Data Governance

Lecture 20 Hands-On: Migrating Workspaces to Unity Catalog & Creating (Managed vs External)

Section 10: Delta Live Tables & Orchestration

Lecture 21 Hands-On : Introduction to Delta Live Tables (DLT) &Creating Your First Pipeline

Lecture 22 Hands-On : Orchestrating Databricks Notebooks with Azure Data Factory (ADF)

Lecture 23 Hands-On : Scheduling Databricks Notebooks using Interactive & Job Clusters

Section 11: Data Engineering with ADF & AutoLoader

Lecture 24 Hands-On : Creating External Tables in SQL DW (Dedicated & Serverless Pools)

Lecture 25 Hands-On : Incremental Data Loads with Directory Listing & File Notifications

Section 12: Interview Preparation & Real-World Use Cases

Lecture 26 Hands-On: Databricks, Spark, PySpark, Delta Lake Interview Questions & Scenarios

Section 13: SQL, PySpark & Regular Expressions for Interviews

Lecture 27 SQL Coding, PySpark Coding, and Regular Expressions – Hands-On Q&A

Data Engineers and Architects who want to master Delta Lake for building scalable data platforms.,Spark and PySpark developers looking to upgrade their skills with Databricks and Delta Lake.,SQL developers, ETL developers, and BI professionals who want to transition into modern data engineering.,Cloud data professionals working with Azure Databricks, Data Lake, and ADF who want hands-on, real-world projects.,Students, freshers, or professionals who have completed my “Databricks & Spark Masterclass: Real-Time Data Engineering” course and want to take the next step into advanced Delta Lake concepts.,Anyone preparing for Databricks, Spark, or Delta Lake interviews with practical, project-based learning.