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210806s2021 xxu| o |||| 0|eng d |
| 005 |
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|a 9781484271827
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|a 10.1007/978-1-4842-7182-7
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|a 004.165
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| 100 |
1 |
|
|a L'Esteve, Ron C.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
|
| 245 |
1 |
4 |
|a The Definitive Guide to Azure Data Engineering :
|b Modern ELT, DevOps, and Analytics on the Azure Cloud Platform /
|c by Ron C. L'Esteve.
|
| 250 |
|
|
|a 1st ed. 2021.
|
| 264 |
|
1 |
|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2021.
|
| 300 |
|
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|a 1 online resource (XXIII, 612 pages 606 illustrations)
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|2 rdamedia
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0 |
|
|a 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 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.
|
| 520 |
|
|
|a 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 variety of Azure data services through hands-on examples.
|
| 650 |
|
0 |
|a Microsoft software.
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| 650 |
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|a Microsoft .NET Framework.
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| 650 |
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|a Database management.
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|a Big data.
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|a Database Management.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I18024
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|a Big Data.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I29120
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| 655 |
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|a Electronic books.
|2 local
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| 710 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer Nature eBook
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9781484271810
|
| 776 |
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|i Printed edition:
|z 9781484271834
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|u http://proxy.library.tamu.edu/login?url=https://doi.org/10.1007/978-1-4842-7182-7
|z Connect to the full text of this electronic book
|t 0
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| 950 |
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|a Professional and Applied Computing (SpringerNature-12059)
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| 950 |
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|a Texas A&M University
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| 998 |
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