DataOps Methodology

Overview

DataOps, as defined by Gartner, is a collaborative data management practice focused on enhancing 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 that influences how data can be optimally provided and updated to meet organizational needs.

The DataOps Methodology aims to enable organizations to utilize a repeatable process to build and deploy analytics and data pipelines.

By adhering to data governance and model management practices, organizations can deliver high-quality enterprise data to empower AI-driven initiatives. Successful implementation of this methodology allows organizations to understand, trust, and use data to drive value.

In the DataOps Methodology course, you will explore best practices for defining a repeatable and business-oriented framework that ensures the delivery of trusted data.

This course is a part of the Data Engineering Specialization, which equips learners with the foundational skills required to become a proficient Data Engineer.

University:

Provider:

Coursera

Categories:

Artificial Intelligence Courses, Big Data Courses, Data Science Courses

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Taught by

Elaine Hanley

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Subjects