Mapping data flows in Azure data factory : building scalable ETL projects in the microsoft cloud / (Record no. 91370)
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| 000 -LEADER | |
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| fixed length control field | 02083nam a22001937a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 231216b2022 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781484291207 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.268 KRO-M |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kromer, Mark |
| 245 ## - TITLE STATEMENT | |
| Title | Mapping data flows in Azure data factory : building scalable ETL projects in the microsoft cloud / |
| Statement of responsibility, etc. | Mark Kromer |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | Apress |
| Date of publication, distribution, etc. | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 194 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 749.00 |
| 500 ## - GENERAL NOTE | |
| General note | Build 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | ETL data |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Azure Data Factory |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Mapping Data Flows |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data Engineering |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | 003-007 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 16/12/2023 | 005.268 KRO-M | 47549 | 13/07/2024 | 16/12/2023 | Books |