Frequently Asked Questions

Data mesh is a decentralised approach to data architecture and organisational structure. Traditional data platforms often centralise data in one place, managed by a single team, leading to bottlenecks and slow processes. Data mesh, on the other hand, treats data as a product, with individual teams responsible for their own data domains. This allows for more flexibility and responsiveness, as each team can manage and evolve their data independently, following common standards and principles.

This decentralised approach enables data governance agility because it promotes a more collaborative and responsive environment. Instead of having a single, monolithic structure that must be navigated for every change or update, data mesh allows for localised control within a broader, cohesive framework. Teams can quickly adapt to new regulations, business needs, or technological changes, while still maintaining alignment with overall organisational goals and compliance requirements. This leads to a more agile and efficient system that can respond to the ever-changing landscape of data management and governance.

It is a colloquial term for a data lake or data warehouse that relies on a large number of resources to maintain data integrity and an inability to operationalise data management practices at scale. 

Yes, this is a common ask. Cloud data warehouses are consumption-based, and so initially, they start off being cost beneficial, especially for new workloads. But be careful as you transition old workloads they require significant compute hours. Note we often see cloud data warehouse vendors recommending a partner or additional apps and services that drive up compute hours. If you are not careful and do not optimise your chosen platform, these data warehouses, just like other cloud services will drain operational expenses.