DataOps Methodology faq

instructor Instructor: Elaine Hanley instructor-icon
duration Duration: duration-icon

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Course Feature Course Overview Pros & Cons Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free

providerProvider:

Coursera

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

29th May, 2023

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [February 21st, 2023]

(Please note this course detail is from the official platform)
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.

Pros & Cons

Pros Cons
  • pros

    Great foundation course

  • pros

    Well constructed

  • pros

    Complete overview

  • pros

    Structured and professional coverage

  • pros

    Explains processes well

  • pros

    Complete content

  • pros

    Great introduction

  • pros

    Clearly and useful

  • cons

    No hands on labs

  • cons

    Repetitive narration

  • cons

    Plain audio lectures

Course Provider

Provider Coursera's Stats at AZClass

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faq FAQ for Big Data Courses

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