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Artículo: AMZ-9365892856
Data Engineering with Azure Databricks: Design, build, and scale intelligent data solutions (English Edition)
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Paperback
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En stock
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0.66 kg
0.66 kg
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Sí
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Nuevo
Producto de
Amazon
Amazon
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USA
USA
Sobre este producto
- As organizations scale, the need for efficient and intelligent data platforms continues to grow. Azure Databricks stands out as a leading platform to deliver scalable data processing, collaborative workflows, and seamless cloud integration. The ability to build scalable pipelines using Apache Spark has become a critical skill for modern data professionals. This book provides a practical and structured journey through the complete data engineering lifecycle using Azure Databricks.It begins with the fundamentals of big data and Spark, then moves into building scalable ETL/ELT pipelines, applying transformations, and enforcing data quality. You will explore architectural patterns in modern data ecosystems and build real-time streaming solutions. Readers will also learn to integrate Databricks with ADF, Power BI, Event Hubs, and other services. The book also covers DevOps and CI/CD using Databricks Asset Bundles.By the end of this book, you will be equipped with the hands-on skills needed to design, build, validate, and manage production-grade data solutions on Azure Databricks. Whether you are a beginner or an experienced professional, you will gain the confidence to engineer robust data solutions that power analytics, AI, and modern data products.What you will learn● Understand big data and data engineering concepts.● Build scalable ETL and ELT pipelines.● Build real-time streaming solutions with Structured Streaming.● Apply data quality checks using Great Expectations.● Implement CI/CD and DevOps with Databricks Asset Bundles.● Performance tuning of Databricks workloads.Who this book is forThis book is designed for data engineers, data architects, data scientists, and IT professionals working with modern data platforms. It is ideal for practitioners looking to build scalable data pipelines and analytical data products, apply robust data quality practices, and master Azure Databricks for real-world data engineering.Table of Contents1. Introduction to Big Data and Data Analytics2. The World of Apache Spark and Databricks3. Setting up Azure and Databricks Environment4. Overview of Databricks Free Edition5. Workspaces, Clusters, and Notebooks6. Data Ingestion and Storage7. Data Exploration and Transformation8. Databricks Delta Tables and Spark SQL9. Data Validation Techniques10. Data Visualization in Databricks11. Real-time Data Processing with Structured Streaming12. DevOps with Databricks13. Monitoring Databricks Applications14. Recommendations for Production Applications
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