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Lakehouse Architecture: A Practical Guide
New
308 students

Lakehouse Architecture: A Practical Guide

Master Delta Lake, Apache Iceberg, Data Lakes, Data Warehouses, Open Table Formats, ETL, Analytics & Governance
Created byMeta Brains
Last updated 7/2026
English
English [Auto],

What you'll learn

  • Understand Lakehouse Architecture principles and how they combine data lakes and data warehouses into a unified analytics platform.
  • Design scalable Lakehouse solutions using open table formats, cloud storage, and modern data engineering best practices.
  • Build reliable data pipelines, implement governance, optimize performance, and manage transactional data workflows.
  • Apply Lakehouse concepts to real-world analytics, machine learning, business intelligence, and enterprise data platforms.

Course content

8 sections • 46 lectures • 4h 30m total length

  • Evolution of Modern Data Platforms7:00

    Explore the evolution from data warehouses and data lakes to the lakehouse, combining scalable data lakes with acid transactions, schema enforcement, and unified governance for bi dashboards and machine learning.

  • Data Lakes vs Data Warehouses5:27

    Compare data lakes and data warehouses, clarifying schema on read versus schema on write, etl, and governance within a lake house architecture.

  • Why the Lakehouse Architecture Emerged7:51
  • Core Principles of Lakehouse Design5:46

    Maintain separation of storage and compute to enable scalable workloads from shared storage, with open formats like Parquet and table formats such as Iceberg or Hudi for reliability and governance.

  • Business Benefits and Use Cases3:14
  • Unified Analytics Concepts7:03

Requirements

  • No prior Lakehouse experience is required. Basic knowledge of databases, SQL, or data analytics is helpful but not mandatory. A computer with internet access is all you need.

Description

Disclaimer : This course contains the use of artificial intelligence.

Lakehouse Architecture has become the modern standard for building scalable, high-performance data platforms that combine the flexibility of data lakes with the reliability and performance of traditional data warehouses. Organizations are rapidly adopting Lakehouse technologies to power business intelligence, real-time analytics, machine learning, and AI applications.

In this comprehensive course, you will gain a practical understanding of Lakehouse Architecture from the ground up. You'll begin by learning the core concepts behind modern data platforms, including the evolution from traditional databases and data warehouses to cloud-native data lakes and unified Lakehouse systems.

The course explores the essential technologies and architectural components that make Lakehouse platforms successful. You'll understand open table formats such as Delta Lake, Apache Iceberg, and Apache Hudi, transactional data management, metadata layers, ACID transactions, schema evolution, time travel, and data versioning. You'll also learn how modern Lakehouse platforms support batch processing, streaming, governance, security, data quality, and enterprise-scale analytics.

Throughout the course, you'll discover best practices for designing scalable Lakehouse architectures, optimizing storage and query performance, building reliable ETL and ELT pipelines, implementing data governance, and supporting AI and machine learning workloads.

Whether you're working with Databricks, Microsoft Fabric, Snowflake, AWS, Google Cloud, or any modern cloud data platform, the architectural principles taught in this course are directly applicable across today's leading technologies.

By the end of this course, you'll be able to confidently design, evaluate, and implement modern Lakehouse solutions for real-world enterprise environments. Whether you're preparing for a new role, upgrading your data engineering skills, or simply staying current with modern data architecture trends, this course provides the knowledge and practical insights you need to succeed.

Who this course is for:

  • This course is designed for data engineers, data analysts, data architects, BI professionals, cloud engineers, software developers, IT professionals, students, and anyone who wants to master modern Lakehouse Architecture for analytics, data engineering, and AI workloads. Beginners with a basic understanding of data concepts are also welcome.

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