The Definitive Guide to Azure Data Engineering
Modern ELT, DevOps, and Analytics on the Azure Cloud Platform
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Beschreibung
Build efficient and scalable batch and real-time data ingestion pipelines, DevOps continuous integration and deployment pipelines, and advanced analytics solutions on the Azure Data Platform. This book teaches you to design and implement robust data engineering solutions using Data Factory, Databricks, Synapse Analytics, Snowflake, Azure SQL database, Stream Analytics, Cosmos database, and Data Lake Storage Gen2. You will learn how to engineer your use of these Azure Data Platform components for optimal performance and scalability. You will also learn to design self-service capabilities to maintain and drive the pipelines and your workloads.
The approach in this book is to guide you through a hands-on, scenario-based learning process that will empower you to promote digital innovation best practices while you work through your organization’s projects, challenges, and needs. The clear examples enable you to use this book as a reference and guide for building data engineering solutions in Azure. After reading this book, you will have a far stronger skill set and confidence level in getting hands on with the Azure Data Platform.
You will learn to:
- Build dynamic, parameterized ELT data ingestion orchestration pipelines in Azure Data Factory
- Create data ingestion pipelines that integrate control tables for self-service ELT
- Implement a reusable logging framework that can be applied to multiple pipelines
- Integrate Azure Data Factory pipelines with a variety of Azure data sources and tools
- Transform data with Mapping Data Flows in Azure Data Factory
- Apply Azure DevOps continuous integration and deployment practices to your Azure Data Factory pipelines and development SQL databases
- Design and implement real-time streaming and advanced analytics solutions using Databricks, Stream Analytics, and Synapse Analytics
- Get started with a varietyof Azure data services through hands-on examples
Build efficient and scalable batch and real-time data ingestion pipelines, DevOps continuous integration and deployment pipelines, and advanced analytics solutions on the Azure Data Platform. This book teaches you to design and implement robust data engineering solutions using Data Factory, Databricks, Synapse Analytics, Snowflake, Azure SQL database, Stream Analytics, Cosmos database, and Data Lake Storage Gen2. You will learn how to engineer your use of these Azure Data Platform components for optimal performance and scalability. You will also learn to design self-service capabilities to maintain and drive the pipelines and your workloads.
The approach in this book is to guide you through a hands-on, scenario-based learning process that will empower you to promote digital innovation best practices while you work through your organization’s projects, challenges, and needs. The clear examples enable you to use this book as a reference and guide for building data engineering solutions in Azure. After reading this book, you will have a far stronger skill set and confidence level in getting hands on with the Azure Data Platform.
What You Will Learn
- Build dynamic, parameterized ELT data ingestion orchestration pipelines in Azure Data Factory
- Create data ingestion pipelines that integrate control tables for self-service ELT
- Implement a reusable logging framework that can be applied to multiple pipelines
- Integrate Azure Data Factory pipelines with a variety of Azure data sources and tools
- Transform data with Mapping Data Flows in Azure Data Factory
- Apply Azure DevOps continuous integration and deployment practices to your Azure Data Factory pipelines and development SQL databases
- Design and implement real-time streaming and advanced analytics solutions using Databricks, Stream Analytics, and Synapse Analytics
- Get started with a variety of Azure data services through hands-on examples
Who This Book Is For
Data engineers and data architects who are interested in learning architectural and engineering best practices around ELT and ETL on the Azure Data Platform, those who are creating complex Azure data engineering projects and are searching for patterns of success, and aspiring cloud and data professionals involved in data engineering, data governance, continuous integration and deployment of DevOps practices, and advanced analytics who want a full understanding of the many different tools and technologies that Azure Data Platform provides
Provides step-by-step examples of Azure data engineering concepts and solutions Promotes a standard for data engineering excellence through quality patterns and practices Leaves readers with valuable skills in Azure data engineering that lead to high-performing solutions
Autor*in
Ron C. L'Esteve
Themen in »The Definitive Guide to Azure Data Engineering«
Azure Data Engineering Azure Data Architecture Cloud Data Engineering Azure Data Factory ETL and ELT Azure Data Platform Azure Databricks Azure Data Ingestion Azure Data Pipelines Snowflake Azure Synapse Data Lake Storage Gen2 Data Ingestion Pipelines Mapping Data Flows Continuous Integration