February 2024
Intermediate
3h 8m
English
Closed Captioning available in German, English, Spanish, French, Italian, Japanese, Korean, Portuguese (Portugal, Brazil), Chinese (Simplified), Chinese (Traditional)
Overview
An increasing number of organizations are adopting data mesh due to its ability to explain and address the frustrations that many organizations experience as they try to get value from data at scale. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance.
This course will show you how to holistically combine the changes to the technical landscape and the operational model required for data mesh to succeed. You will explore domain ownership, data as a product, how to create a strong developer experience, and federated governance. Once you have completed the course, you will be able to understand the different elements of data mesh in practice and have the confidence to drive data mesh adoption in your company.
What you’ll learn and how you can apply it:
- Understand the intent and value behind the foundational elements of data mesh, and have the confidence to participate in a data mesh adoption.
This course is for you because:
- You are or are interested in becoming, involved in a data mesh adoption.
- You have a stake in successful data use at scale–you’re a machine learning practitioner, a data engineer, an engineering manager with responsibility for data or a leader of a technical organization.
- You are interested in problems at the intersection of business, data and collaboration.
Prerequisites
Either:
- Practical experience implementing distributed transactional systems.
- Practical experience implementing analytical systems.
Recommended follow-up:
- Read Designing Data-Intensive Applications to deepen your technical understanding of data systems at scale.
- Read Creating a Data-Driven Organisation for a broader look at how to structure an organization to maximize value from data.
- Read Fundamentals of Data Observability to learn about how to automate data quality checks.
- Read Enterprise Data Catalog for a deep dive into the discovery part of data management.
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