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

指責

書目名稱Data Lake Analytics on Microsoft Azure影響因子(影響力)




書目名稱Data Lake Analytics on Microsoft Azure影響因子(影響力)學科排名




書目名稱Data Lake Analytics on Microsoft Azure網絡公開度




書目名稱Data Lake Analytics on Microsoft Azure網絡公開度學科排名




書目名稱Data Lake Analytics on Microsoft Azure被引頻次




書目名稱Data Lake Analytics on Microsoft Azure被引頻次學科排名




書目名稱Data Lake Analytics on Microsoft Azure年度引用




書目名稱Data Lake Analytics on Microsoft Azure年度引用學科排名




書目名稱Data Lake Analytics on Microsoft Azure讀者反饋




書目名稱Data Lake Analytics on Microsoft Azure讀者反饋學科排名

flammable

Book 2020n help you start your journey into the data engineer world as it provides an overview of advanced data analytics and touches on data science concepts and various artificial intelligence and machine learning technologies available on Microsoft Azure..What Will You Learn.You will understand the:.Conce

冷淡周邊

Data Lake Analytics Concepts,nessing the power of this data. Not only that, with the democratization of .rtificial .ntelligence and .achine .earning, building predictions has become easier. The infusion of AI/ML with data has given lots of advantages to plan future requirements or actions. Some of the classic use cases are cust

圣歌

Building Blocks of Data Analytics,t-moving consumer goods) are heavily dependent on their data analytics solutions. A few examples of the outcomes of data analytics are customer 360-degree, real-time recommendations, fraud analytics, and predictive maintenance solutions. This chapter is designed to share an overview of the building

Eructation

擦試不掉

擦試不掉

Data Storage,s 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.

Absenteeism

Data Preparation and Training Part I,ces 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

確定

Data Preparation and Training Part II,es brought lots of innovative technologies for data analytics. How the transformation from data analytics and enterprise data warehouse to modern data warehouse and advanced data analytics has happened. In part I of the prep and train phase, the discussion was on the modern data warehouse. In this c

吼叫

Model and Serve, 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.