1 option
Data Engineering with Azure Databricks: Design, Build, and Optimize Scalable Data Pipelines and Analytics Solutions with Azure Databricks.
- Format:
- Book
- Author/Creator:
- Foshin, Dmitry
- Language:
- English
- Subjects (All):
- Big data.
- Microsoft Azure (Computing platform).
- Physical Description:
- 1 online resource (412 p.)
- Place of Publication:
- Birmingham, England Packt Publishing 2026
- Summary:
- Discover how to build robust, production-grade data pipelines and analytics solutions using Azure Databricks. In this book, you'll explore tools like Apache Spark, Delta Lake, and Unity Catalog to design scalable data workflows and enable data-driven decision-making. What this Book will help me do Set up Azure Databricks for complex data engineering tasks. Develop batch and streaming data ingestion workflows. Optimize Apache Spark applications for high efficiency. Implement comprehensive security and governance with Unity Catalog. Integrate machine learning workflows using Databricks-native tools. Author(s) Dmitry Foshin, Dmitry Anoshin, Tonya Chernyshova, and Xenia Ireton are seasoned experts in data engineering and cloud technologies. With years of experience designing large-scale data solutions, they bring practical insights and best practices to every chapter. Their passion for sharing technical knowledge ensures this book is an invaluable resource. Who is it for? This book is ideal for data engineers, architects, developers, and technical professionals eager to deepen their expertise in Azure Databricks. Whether you're automating workflows, developing modern analytics platforms, or applying AI for business intelligence, this book can bridge the gap between knowledge and application.
- Contents:
- Table of Contents The Role of Azure Databricks in Modern Data Engineering Setting up an End-To-End Azure Databricks Environment Data Ingestion Strategies for Azure Databricks Data Engineering with Apache Spark Building Real-Time Data Pipelines Working with Delta Lake: ACID Transactions and Schema Evolution Automating Data Systems with Lakeflow Spark Declarative Pipelines Orchestrating Data Workflows: From Notebooks to Production CI/CD and DevOps for Azure Databricks Optimizing Query Performance and Cost Management Security, Compliance, and Data Governance Machine Learning and AI on Databricks.
- ISBN:
- 9781806106363
- OCLC:
- 1588210505
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.