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001438801 0247_ $$a10.1007/978-1-4842-7182-7$$2doi
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001438801 050_4 $$aQA76.9.D3$$bL47 2021
001438801 08204 $$a005.75/65$$223
001438801 1001_ $$aL'Esteve, Ron C.,$$eauthor.
001438801 24514 $$aThe definitive guide to Azure data engineering :$$bmodern ELT, DevOps, and analytics on the Azure Cloud Platform /$$cRon C. L'Esteve.
001438801 264_1 $$a[Berkeley] :$$bApress,$$c[2021]
001438801 264_4 $$c©2021
001438801 300__ $$a1 online resource :$$billustrations
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001438801 5050_ $$aIntroduction -- 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 Anomaly Detection -- 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.
001438801 506__ $$aAccess limited to authorized users.
001438801 520__ $$aBuild 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 variety of Azure data services through hands-on examples.
001438801 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 24, 2021).
001438801 63000 $$aMicrosoft Azure SQL Database.
001438801 650_0 $$aDatabase management.
001438801 650_0 $$aCloud computing.
001438801 650_6 $$aBases de données$$xGestion.
001438801 650_6 $$aInfonuagique.
001438801 655_0 $$aElectronic books.
001438801 77608 $$iPrint version:$$z1484271815$$z9781484271810$$w(OCoLC)1253474667
001438801 852__ $$bebk
001438801 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-7182-7$$zOnline Access$$91397441.1
001438801 909CO $$ooai:library.usi.edu:1438801$$pGLOBAL_SET
001438801 980__ $$aBIB
001438801 980__ $$aEBOOK
001438801 982__ $$aEbook
001438801 983__ $$aOnline
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