ISBN: 9781806679775 - building trusted data platforms with azure databricks and genai: a hands-on guide to creating governed data products in a lakehouse (12 risultati)

Paperback or Softback. Condizione: New. Building Trusted Data Platforms with Azure Databricks and GenAI - Second Edition: A Hands-On Guide to Creating Governed Data Products in a Lakehouse. Book.

Condizione: New.

Paperback. Condizione: New. 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 FeaturesDiscover characteristics of future-ready platforms - data mesh, automation, and observabilityDesign trustworthy data products with contracts, federated governance, and decentralized ownershipUnderstand how GenAI accelerates Lakehouse development and enables self-service analyticsBook DescriptionDiscover 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 learnFuture-ready platforms: data mesh, automation, observabilityDesign trusted data products with contracts and governanceBuild Lakehouses with medallion architecture: bronze, silver, goldApply Unity Catalog for governance and endorsed datasetsImplement MDM using linkages, survivorship, and entity resolutionDevelop real-time and streaming pipelines at scaleEnable governed self-service analytics for business usersUse GenAI to generate code with Claude and Databricks GenieWho this book is forThis 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.…

Paperback. Condizione: New. 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 FeaturesDiscover characteristics of future-ready platforms - data mesh, automation, and observabilityDesign trustworthy data products with contracts, federated governance, and decentralized ownershipUnderstand how GenAI accelerates Lakehouse development and enables self-service analyticsBook DescriptionDiscover 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 learnFuture-ready platforms: data mesh, automation, observabilityDesign trusted data products with contracts and governanceBuild Lakehouses with medallion architecture: bronze, silver, goldApply Unity Catalog for governance and endorsed datasetsImplement MDM using linkages, survivorship, and entity resolutionDevelop real-time and streaming pipelines at scaleEnable governed self-service analytics for business usersUse GenAI to generate code with Claude and Databricks GenieWho this book is forThis 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.…

Paperback. Condizione: new. Paperback. 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 FeaturesDiscover characteristics of future-ready platforms - data mesh, automation, & observabilityDesign trustworthy data products with contracts, federated governance, and decentralized ownershipUnderstand how GenAI accelerates Lakehouse development and enables selfservice analyticsBook DescriptionDiscover the defining hallmarks of futureready data platforms, including data mesh architectures, intelligent automation, and endtoend 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 analyticsready assets governed through Unity Catalog. Youll gain practical guidance on MDM linkages, survivorship rules, and entity resolution to ensure consistent master data across domains. It also covers realtime and streaming pipelines that integrate seamlessly with the Lakehouse. We focus on selfservice 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 learnFutureready platforms: data mesh, automation, observabilityDesign trusted data products with contracts and governanceBuild Lakehouses with medallion architecture: bronze, silver, goldApply Unity Catalog for governance and endorsed datasetsImplement MDM using linkages, survivorship, and entity resolutionDevelop realtime and streaming pipelines at scaleEnable governed selfservice analytics for business usersUse GenAI to generate code with Claude and Databricks GenieWho this book is forThis book is crafted for aspiring data and AI/ML architects, engineers and analysts starting their data engineering journey and seeking a practical, handson guide to building scalable, clouddriven data platforms. Its 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. Discover traits of data platforms, data mesh, automation, and observability. Design trusted data products with contracts, federated governance, and decentralized ownership, and explore how GenAI accelerates Lakehouse development and analytics. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Paperback. Condizione: new. Paperback. 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 FeaturesDiscover characteristics of future-ready platforms - data mesh, automation, & observabilityDesign trustworthy data products with contracts, federated governance, and decentralized ownershipUnderstand how GenAI accelerates Lakehouse development and enables selfservice analyticsBook DescriptionDiscover the defining hallmarks of futureready data platforms, including data mesh architectures, intelligent automation, and endtoend 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 analyticsready assets governed through Unity Catalog. Youll gain practical guidance on MDM linkages, survivorship rules, and entity resolution to ensure consistent master data across domains. It also covers realtime and streaming pipelines that integrate seamlessly with the Lakehouse. We focus on selfservice 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 learnFutureready platforms: data mesh, automation, observabilityDesign trusted data products with contracts and governanceBuild Lakehouses with medallion architecture: bronze, silver, goldApply Unity Catalog for governance and endorsed datasetsImplement MDM using linkages, survivorship, and entity resolutionDevelop realtime and streaming pipelines at scaleEnable governed selfservice analytics for business usersUse GenAI to generate code with Claude and Databricks GenieWho this book is forThis book is crafted for aspiring data and AI/ML architects, engineers and analysts starting their data engineering journey and seeking a practical, handson guide to building scalable, clouddriven data platforms. Its 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. Discover traits of data platforms, data mesh, automation, and observability. Design trusted data products with contracts, federated governance, and decentralized ownership, and explore how GenAI accelerates Lakehouse development and analytics. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. 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 FeaturesDiscover characteristics of future-ready platforms - data mesh, automation, & observabilityDesign trustworthy data products with contracts, federated governance, and decentralized ownershipUnderstand how GenAI accelerates Lakehouse development and enables selfservice analyticsBook DescriptionDiscover the defining hallmarks of futureready data platforms, including data mesh architectures, intelligent automation, and endtoend 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 analyticsready assets governed through Unity Catalog. Youll gain practical guidance on MDM linkages, survivorship rules, and entity resolution to ensure consistent master data across domains. It also covers realtime and streaming pipelines that integrate seamlessly with the Lakehouse. We focus on selfservice 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 learnFutureready platforms: data mesh, automation, observabilityDesign trusted data products with contracts and governanceBuild Lakehouses with medallion architecture: bronze, silver, goldApply Unity Catalog for governance and endorsed datasetsImplement MDM using linkages, survivorship, and entity resolutionDevelop realtime and streaming pipelines at scaleEnable governed selfservice analytics for business usersUse GenAI to generate code with Claude and Databricks GenieWho this book is forThis book is crafted for aspiring data and AI/ML architects, engineers and analysts starting their data engineering journey and seeking a practical, handson guide to building scalable, clouddriven data platforms. Its 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. Discover traits of data platforms, data mesh, automation, and observability. Design trusted data products with contracts, federated governance, and decentralized ownership, and explore how GenAI accelerates Lakehouse development and analytics. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Discover traits of data platforms, data mesh, automation, and observability. Design trusted data products with contracts, federated governance, and decentralized ownership, and explore how GenAI accelerates Lakehouse development and analytics.…

Taschenbuch. Condizione: Neu. Building Trusted Data Platforms with Azure Databricks and GenAI - Second Edition | A Hands-On Guide to Creating Governed Data Products in a Lakehouse | Manoj Kukreja | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781806679775 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…