Lakehouse Architecture and Delta Lake with Databricks

Overview

Design and implement production ready Lakehouse architectures using Delta Lake and Databricks. By the end of this course, you will be able to build multi layer Medallion pipelines including Bronze, Silver, and Gold layers, manage ACID transactions, enforce and evolve schemas, implement Change Data Capture, and optimize Delta tables for performance using data skipping, compaction, and Liquid Clustering. You will also learn to unify batch and streaming workloads while ensuring reliability, scalability, and recoverability in enterprise environments. This course stands out by going beyond Delta Lake syntax and focusing on end to end Lakehouse engineering, from architectural design patterns to production optimization and concurrency control. Through structured modules and hands on implementation, you will gain practical experience designing scalable data platforms that support both BI analytics and machine learning workloads. If you are a data engineer, analytics engineer, or platform architect looking to modernize legacy data warehouses or data lakes, this course provides the applied skills required to build efficient, cost effective, and future ready data infrastructure on Databricks. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming.

Syllabus

Modern Data Architecture Fundamentals

This module introduces the evolution of modern data platforms, from traditional warehouses and data lakes to the unified Lakehouse architecture. Learners explore foundational concepts of Databricks, Apache Spark, and Delta Lake that enable scalable, reliable, and governed data processing.

Core Delta Lake Concepts and Operations

This module focuses on the core operational capabilities of Delta Lake, including storage architecture, metadata management, transactional processing, and schema control. Learners gain hands-on experience with CRUD operations, incremental data pipelines, time travel, and streaming to build reliable, production-ready data workflows.

Lakehouse Architecture and Performance Optimization

This module focuses on designing scalable Lakehouse architectures using Medallion patterns and optimizing Delta Lake for performance and cost efficiency. Learners build multi-layer data pipelines and apply advanced optimization techniques to support BI and machine learning workloads.

Advanced Delta Lake and Production Readiness

This module focuses on designing scalable Lakehouse architectures using Medallion patterns and optimizing Delta Lake for performance and cost efficiency. Learners build multi-layer data pipelines and apply advanced optimization techniques to support BI and machine learning workloads.