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DataOps Methodology

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.

状态:Data Management
状态:Data Pipelines
初级课程小时

精选评论

SR

5.0评论日期:Nov 21, 2024

Absolutely amazing content! structural and give you an overall view of data management

DT

5.0评论日期:Oct 21, 2021

Really enjoyed this, explains all the proccesses really well

BS

5.0评论日期:Jul 26, 2024

Very clearly explained the DataOps concepts. Thank you very much.

LH

5.0评论日期:Jul 31, 2022

Great over view and good breakdown of the concepts

CA

4.0评论日期:Sep 27, 2022

The content was very complete. The only opportunity of improvement is the narration of the lectures. The lack of changes in the voice tone can make the audio lectures very repetitive and plain.

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