Building Trusted Data Platforms with Azure Databricks and GenAI
Building Trusted Data Platforms with Azure Databricks and GenAI is a hands-on guide to governed data products and Lakehouse design. Its scope ranges from medallion architecture, Unity Catalog and master data management to streaming, observability, data mesh and GenAI coding tools. The listing expects working knowledge of Python, Spark and SQL, so it is aimed at readers ready to build on those foundations rather than complete beginners.
At a glance
- Covers medallion Lakehouse patterns, including bronze, silver and gold layers, alongside Delta Lake and Unity Catalog.
- Explains data contracts, federated governance and decentralized domain ownership for governed data products.
- Addresses MDM linkages, survivorship rules, entity resolution, streaming pipelines and observability.
- Includes GenAI-assisted code generation with Claude Code and Databricks Genie Code, according to the listing.
Reference price: $59.99
Price checked: Nov 14, 2026
View offerAhead of Black FridayExplore the selectionPros and cons
Pros
- Brings platform architecture, data governance, master data management and analytics into one broad guide.
- The listed contents connect data engineering concepts to practical Lakehouse layers and data product workflows.
- The intended audience and assumed Python, Spark and SQL background are stated clearly.
Technical specifications
8 specifications| Publisher | Packt Publishing |
|---|---|
| Edition | 2nd ed. |
| Publication date | August 7, 2026 |
| Language | English |
| Print length | 774 pages |
| ISBN-13 | 978-1806679775 |
| Weight listed | 2.88 pounds |
|---|---|
| Dimensions | 7.5 x 1.75 x 9.25 inches |
Who is it for?
A good fit for
- Data engineers and architects familiar with Python, Spark and SQL who want to develop Azure Databricks and Lakehouse skills.
- Analytics and AI/ML practitioners interested in data mesh, MDM, governed self-service analytics or GenAI-assisted platform work.
Less suited to
- Readers seeking a beginner introduction with no prior working knowledge of Python, Spark and SQL.
- Readers looking for a narrowly focused reference on only one topic, such as streaming or MDM, rather than a broad platform guide.
Customer reviews on Amazon
- 5 stars
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The listing shows uniformly five-star aggregate ratings, but the rating sample is small and no written customer feedback is available here. Ratings alone do not establish what readers liked or how useful the book is for a particular background. Prospective readers can judge the stated scope against their needs, especially its broad coverage and Python, Spark and SQL prerequisites.
Frequently asked questions
What background does the listing recommend?
It expects working knowledge of Python, Spark and SQL, and describes the book for readers familiar with PySpark.
Which Lakehouse and governance topics are covered?
The listing describes medallion architecture, Delta Lake, Unity Catalog, data contracts, federated governance, decentralized ownership and endorsed datasets.
Does the book cover master data management and streaming?
Yes. The described material includes MDM linkages, survivorship rules, entity resolution, and real-time and streaming pipelines.
How does GenAI fit into the book?
The listing says it covers code generation for platform development using Claude Code and Databricks Genie Code.
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