Understand why current data architectures are not scalable
Know the shifts data mesh introduces to respond gracefully to change and complexity
The promise and principles of data mesh
Practical examples and tips on implementing the data mesh
A monolithic architecture, technology, and organizational structure are not suitable for analytical data management of large-scale and complex organizations.
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
We’ve started to see returns of investment over data to plateau, and data teams getting stressed with the challenges of generating value from the data.
Data mesh is that shift in the mindset which aims and promises to:
- Respond gracefully to change and complexity
- Sustain agility in the face of growth
- Increase value of data over cost
This illustrative course gives you finer understanding of the principles of data mesh, along with the practical examples and tips on implementing the data mesh.
We’ll also look into one real world situation as how the data culture in an organisation got transformed with data mesh.
