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001449035 020__ $$a9781484286128$$q(electronic bk.)
001449035 020__ $$a148428612X$$q(electronic bk.)
001449035 020__ $$z1484286111
001449035 020__ $$z9781484286111
001449035 0247_ $$a10.1007/978-1-4842-8612-8$$2doi
001449035 035__ $$aSP(OCoLC)1342535043
001449035 040__ $$aYDX$$beng$$erda$$cYDX$$dORMDA$$dEBLCP$$dGW5XE$$dYDX$$dN$T$$dOCLCF$$dUKAHL$$dOCLCQ
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001449035 050_4 $$aQA76.9.D37$$bK76 2022
001449035 08204 $$a005.75/65$$223/eng/20220830
001449035 1001_ $$aKromer, Mark,$$eauthor.
001449035 24510 $$aMapping data flows in Azure data factory :$$bbuilding scalable ETL projects in the Microsoft Cloud /$$cMark Kromer.
001449035 264_1 $$aNew York, NY :$$bAPRESS,$$c2022.
001449035 300__ $$a1 online resource
001449035 336__ $$atext$$btxt$$2rdacontent
001449035 337__ $$acomputer$$bc$$2rdamedia
001449035 338__ $$aonline resource$$bcr$$2rdacarrier
001449035 500__ $$aIncludes index.
001449035 5050_ $$aIntroduction -- Part I. Getting Started with Azure Data Factory and Mapping Data Flows -- 1. Introduction to Azure Data Factory -- 2. Introduction to Mapping Data Flows -- Part II. Designing Scalable ETL Jobs with ADF Mapping Data Flows -- 3. Build Your First Pipeline -- 4. Common Pipeline Patterns -- 5. Design Your First Mapping Data Flow -- 6. Common Data Flow Patterns -- 7. Debugging Mapping Data Flows -- 8. Data Pipelines with Data Flows -- Part III. Operationalize your ETL Data Pipelines -- 9. CI/CD and Scheduling -- 10. Monitoring, Management, and Security -- Part IV. Sample Project -- 11. Build a New ETL Project in ADF using Mapping Data Flows -- 12. End-to-End Review of the ADF Project.
001449035 506__ $$aAccess limited to authorized users.
001449035 520__ $$aBuild scalable ETL data pipelines in the cloud using Azure Data Factory's Mapping Data Flows. Each chapter of this book addresses different aspects of an end-to-end data pipeline that includes repeatable design patterns based on best practices using ADF's code-free data transformation design tools. The book shows data engineers how to take raw business data at cloud scale and turn that data into business value by organizing and transforming the data for use in data science projects and analytics systems. The book begins with an introduction to Azure Data Factory followed by an introduction to its Mapping Data Flows feature set. Subsequent chapters show how to build your first pipeline and corresponding data flow, implement common design patterns, and operationalize your result. By the end of the book, you will be able to apply what you've learned to your complex data integration and ETL projects in Azure. These projects will enable cloud-scale big analytics and data loading and transformation best practices for data warehouses. What You Will Learn Build scalable ETL jobs in Azure without writing code Transform big data for data quality and data modeling requirements Understand the different aspects of Azure Data Factory ETL pipelines from datasets and Linked Services to Mapping Data Flows Apply best practices for designing and managing complex ETL data pipelines in Azure Data Factory Add cloud-based ETL patterns to your set of data engineering skills Build repeatable code-free ETL design patterns Who This Book Is For Data engineers who are new to building complex data transformation pipelines in the cloud with Azure; and data engineers who need ETL solutions that scale to match swiftly growing volumes of data.
001449035 63000 $$aWindows Azure.
001449035 650_0 $$aData warehousing.
001449035 650_0 $$aDatabase management.
001449035 650_0 $$aInformation storage and retrieval systems$$xData processing.
001449035 650_0 $$aCloud computing.
001449035 655_0 $$aElectronic books.
001449035 77608 $$iPrint version:$$z1484286111$$z9781484286111$$w(OCoLC)1330407001
001449035 852__ $$bebk
001449035 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-8612-8$$zOnline Access$$91397441.1
001449035 909CO $$ooai:library.usi.edu:1449035$$pGLOBAL_SET
001449035 980__ $$aBIB
001449035 980__ $$aEBOOK
001449035 982__ $$aEbook
001449035 983__ $$aOnline
001449035 994__ $$a92$$bISE