Building Trusted Data Platforms with Azure Databricks and GenAI: A Hands-On Guide to Creating Governed Data Products in a Lakehouse - Image 1

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Packt Publishing Building Trusted Data Platforms with Azure Databricks and GenAI: A Hands-On Guide to Creating Governed Data Products in a Lakehouse

Name: Building Trusted Data Platforms with Azure Databricks and GenAI: A Hands-On Guide to Creating Governed Data Products in a Lakehouse
Brand: Packt Publishing
Price: 44.99 GBP
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Beginners & Introduction

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Description

A practical guide to building a modern, GenAI-powered data platform with a Lakehouse foundation, covering MDM, data mesh, AI enablement, streaming pipelines, observability, and cloud-driven architectures for trusted analytics. Key Features Discover characteristics of future-ready platforms - data mesh, automation, & observability Design trustworthy data products with contracts, federated governance, and decentralized ownership Understand how GenAI accelerates Lakehouse development and enables self‑service analytics Book Description Discover the defining hallmarks of future‑ready data platforms, including data mesh architectures, intelligent automation, and end‑to‑end data observability. Learn how to design and deliver trusted data products through data contracts, federated governance, decentralized domain ownership, and endorsed datasets. The book explores modern Lakehouse patterns with a strong focus on the medallion architecture, explaining how bronze, silver, and gold layers transform raw data into analytics‑ready assets governed through Unity Catalog. You’ll gain practical guidance on MDM linkages, survivorship rules, and entity resolution to ensure consistent master data across domains. It also covers real‑time and streaming pipelines that integrate seamlessly with the Lakehouse. We focus on self‑service analytics, showing how governed data products let business users explore, analyze, and derive insights independently with confidence. Finally, understand how GenAI accelerates platform development through automated code generation using tools like Claude Code and Databricks Genie Code, enabling faster pipeline creation, governance, and analytics delivery. What you will learn Future‑ready platforms: data mesh, automation, observability Design trusted data products with contracts and governance Build Lakehouses with medallion architecture: bronze, silver, gold Apply Unity Catalog for governance and endorsed datasets Implement MDM using linkages, survivorship, and entity resolution Develop real‑time and streaming pipelines at scale Enable governed self‑service analytics for business users Use GenAI to generate code with Claude and Databricks Genie Who this book is for This book is crafted for aspiring data and AI/ML architects, engineers and analysts starting their data engineering journey and seeking a practical, hands‑on guide to building scalable, cloud‑driven data platforms. It’s ideal for professionals familiar with PySpark who want to design modern Lakehouse architectures using Delta Lake, while learning MDM, data mesh, AI enablement, streaming pipelines, automation, and data observability. A working knowledge of Python, Spark, and SQL is expected. Table of Contents The Story of Data Engineering and Analytics Discovering Data Platforms Data Engineering on Microsoft Azure Designing Future Data Platforms Understanding Databricks Understanding Medallion Architecture and Delta Lake Understanding Modern Data Pipelines Data Ingestion Stage: Building the Landing Zone Building the Bronze Layer of the Medallion Architecture using Gen AI Building the Silver Layer of the Medallion Architecture using Gen AI Building the Next-Gen Data Analytics Gold Layer Governing Data Products

Product Specifications

Brand: Packt Publishing
Format: paperback
ASIN: 1806679779
Category: Books > Subjects > Computing & Internet > Databases > Beginners & Introduction
Domain: Amazon UK
Release Date: 07 August 2026
Listed Since: 15 May 2026

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Packt Publishing Building Trusted Data Platforms with Azure Databricks and GenAI: A Hands-On Guide to Creating Governed Data Products in a Lakehouse

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