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
Auflage
Sprache
Verlagsort
Zielgruppe
Produkt-Hinweis
Broschur/Paperback
Klebebindung
Illustrationen
606
606 s/w Abbildungen
XXIII, 612 p. 606 illus.
Maße
Höhe: 254 mm
Breite: 178 mm
Dicke: 33 mm
Gewicht
ISBN-13
978-1-4842-7181-0 (9781484271810)
DOI
10.1007/978-1-4842-7182-7
Schweitzer Klassifikation
Ron L'Esteve is a professional author residing in Chicago, IL, USA. His passion for Azure Data Engineering stems from his deep experience with implementing, leading, and delivering Azure Data projects for numerous clients. He is a trusted architectural leader and digital innovation strategist, responsible for scaling key data architectures, defining the road map and strategy for the future of data and business intelligence (BI) needs, and challenging customers to grow by thoroughly understanding the fluid business opportunities and enabling change by translating them into high quality and sustainable technical solutions that solve the most complex business challenges and promote digital innovation and transformation. Ron has been an advocate for data excellence across industries and consulting practices, while empowering self-service data, BI, and AI through his contributions to the Microsoft technical community.
Introduction.- Part I. Getting Started.- 1. The Tools and Pre-Requisites.- 2. Data Factory vs SSIS vs Databricks.- 3. Design a Data Lake Storage Gen2 Account.- Part II. Azure Data Factory for ELT.- 4. Dynamically Load SQL Database to Data Lake Storage Gen 2.- 5. Use COPY INTO to Load Synapse Analytics Dedicated SQL Pool.- 6. Load Data Lake Storage Gen2 Files into Synapse Analytics Dedicated SQL Pool.- 7. Create and Load Synapse Analytics Dedicated SQL Pool Tables Dynamically.- 8. Build Custom Logs in SQL Database for Pipeline Activity Metrics.- 9. Capture Pipeline Error Logs in SQL Database.-10. Dynamically Load Snowflake Data Warehouse.-11. Mapping Data Flows for Data Warehouse ETL.- 12. Aggregate and Transform Big Data Using Mapping Data Flows.- 13. Incrementally Upsert Data.-14. Loading Excel Sheets into Azure SQL Database Tables.-15. Delta Lake.- Part III. Real-Time Analytics in Azure.- 16. Stream Analytics AnomalyDetection.- 17. Real-time IoT Analytics Using Apache Spark.- 18. Azure Synapse Link for Cosmos DB.- Part IV.
DevOps for Continuous Integration and Deployment.- 19. Deploy Data Factory Changes.- 20. Deploy SQL Database.- Part V. Advanced Analytics.- 21. Graph Analytics Using Apache Spark's GraphFrame API.- 22. Synapse Analytics Workspaces.- 23. Machine Learning in Databricks.- Part VI. Data Governance.- 24. Purview for Data Governance.
