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Titlebook: Data Lake Analytics on Microsoft Azure; A Practitioner‘s Gui Harsh Chawla,Pankaj Khattar Book 2020 Harsh Chawla and Pankaj Khattar 2020 Azu

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发表于 2025-3-23 10:30:11 | 显示全部楼层
Summary,ology stack on Microsoft Azure, this book could be an excellent source of information on various architecture patterns and services that could be consumed to build data pipelines. However, if the reader is a beginner into the field of data engineering, this book has lots of exercises along with the
发表于 2025-3-23 14:46:50 | 显示全部楼层
Sergio Lara-Bercial,John Bales,Julian Northtions can build, scale, and consume these solutions with a faster pace and economical cost. Since there is a fair understanding of data lakes and data analytics basics by now, this chapter discusses the role of a public cloud to disrupt the market and to accelerate the adoption of data analytics solutions.
发表于 2025-3-23 20:13:38 | 显示全部楼层
Cristina Bianchi,Maureen Steeles applications through an ETL process for further processing. In this chapter, the discussion is around what role the data storage layer in data analytics plays and various storage options available on Microsoft Azure.
发表于 2025-3-23 22:52:26 | 显示全部楼层
Oana A. David,Radu Şoflău,Silviu Matu through visualization or any dependent applications. The entire data journey is planned, based on the target use case. In this chapter, the discussion is on the various scenarios that are applicable in this phase, and how to decide on technologies based on the cost and efficiency.
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发表于 2025-3-24 13:09:25 | 显示全部楼层
Sergio Lara-Bercial,John Bales,Julian Northtions can build, scale, and consume these solutions with a faster pace and economical cost. Since there is a fair understanding of data lakes and data analytics basics by now, this chapter discusses the role of a public cloud to disrupt the market and to accelerate the adoption of data analytics sol
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发表于 2025-3-24 23:53:42 | 显示全部楼层
Oana A. David,Radu Şoflău,Silviu Matuces is merged and crunched together (Figure 6-1). The transformed data further gets infused with machine learning models or is sent to the model and serve phase. The entire data journey is planned, based on the target use case. This phase has been split into two chapters. In this chapter, the discus
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