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230602 ||| eng |
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|a HF5548.2
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|a Jayanthi, Satish
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|a Automating data transformations
|c by Satish Jayanthi and Armon Petrossian
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| 250 |
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|a First edition
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| 260 |
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|a Sebastopol, CA
|b O'Reilly Media, Inc.
|c 2023
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| 300 |
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|a 47 pages
|b illustrations
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| 653 |
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|a Gestion / Informatique
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| 653 |
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|a Cloud computing / fast
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| 653 |
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|a Infonuagique
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| 653 |
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|a Cloud computing / http://id.loc.gov/authorities/subjects/sh2008004883
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| 653 |
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|a Business / Data processing / http://id.loc.gov/authorities/subjects/sh85018264
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| 653 |
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|a Business / Data processing / fast
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| 700 |
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|a Petrossian, Armon
|e author
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 9781098147587
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|u https://learning.oreilly.com/library/view/~/9781098147594/?ar
|x Verlag
|3 Volltext
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|a 658/.05
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|a 330
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|a The modern data stack has evolved rapidly in the past decade. Yet, as enterprises migrate vast amounts of data from on-premises platforms to the cloud, data teams continue to face limitations executing data transformation at scale. Data transformation is an integral part of the analytics workflow--but it's also the most time-consuming, expensive, and error-prone part of the process. In this report, Satish Jayanthi and Armon Petrossian examine key concepts that will enable you to automate data transformation at scale. IT decision makers, CTOs, and data team leaders will explore ways to democratize data transformation by shifting from activity-oriented to outcome-oriented teams--from manufacturing-line assembly to an approach that lets even junior analysts implement data with only a brief code review
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