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學生對 IBM 提供的 DataOps Methodology 的評價和反饋

4.7
20 個評分
7 條評論

課程概述

DataOps is defined by Gartner as "a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization. Much like DevOps, DataOps is not a rigid dogma, but a principles-based practice influencing how data can be provided and updated to meet the need of the organization’s data consumers.” The DataOps Methodology is designed to enable an organization to utilize a repeatable process to build and deploy analytics and data pipelines. By following data governance and model management practices they can deliver high-quality enterprise data to enable AI. Successful implementation of this methodology allows an organization to know, trust and use data to drive value. In the DataOps Methodology course you will learn about best practices for defining a repeatable and business-oriented framework to provide delivery of trusted data. This course is part of the Data Engineering Specialization which provides learners with the foundational skills required to be a Data Engineer....

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1 - DataOps Methodology 的 7 個評論(共 7 個)

創建者 Guy P

2022年3月17日

V​ery well constructed, with right level of mix between high level view and details. It provides a very complete overview and refresh of what is Data Ops. Congratulations to the team. I will recommend the course

創建者 Paul R

2022年1月16日

very structured and professional coverage of DataOps ; very valuable, thank you

創建者 Dean T

2021年10月22日

Really enjoyed this, explains all the proccesses really well

創建者 Bruno M d O J

2022年3月9日

Excel

創建者 Gustavo M

2022年3月17日

This is a great foundation course. I just wished that it had hands-on labs and exercises to cement the knowledge. I am dissapointed, as this class for its simplicity should have been a Coursera Plus course, considering the size of the content. Either way, it has good material.

創建者 Boris G

2022年5月6日

Great introduction to DataOps

創建者 Hamdan S A

2022年4月14日

good