How do you explain the concept of data mesh to business domains?
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Data mesh is a new way of managing data that involves creating an open and decentralized network of interconnected data stores and services. It is a new approach to data management that breaks down the silos of data, allowing for more efficient and effective access to data across the enterprise.
Data mesh emphasizes the importance of data as a shared resource, with each component of the data mesh providing a unique view or perspective on the data. By connecting these components in a secure way, it allows for greater collaboration, faster problem solving, and more efficient business decisions.
Data mesh enables organizations to integrate data from multiple sources, to provide a single source of truth and to rapidly develop applications that use data from multiple sources. This approach is ideal for organizations that need to take advantage of the latest data technologies, while maintaining the control and governance of their data.
I would agree with that definition, and would add that the sources of data that are brought into the "mesh" still need to adhere to good old-fashioned requirements of good data governance, quality, and custodianship as the garbage-in-garbage-out rule still very much applies. Data mesh is not a salve for solving these foundational issues around data architecture practices.
With the diverse range of decision intelligence encompassing ( BI, ML, AI and even GenAI) alongside the varied data types including structured, semi-structured, and unstructured data, adaptation to Data Lakehouse is imperative. It is not longer for organizations to lock themselves in structure data warehouses.
The expectation now is that any type and source of data should be readily available for consumption, boosting business agility and accommodating the growing variety of requirements and use case development for the business domains.
However, each business unit embarks on its unique analytics journey in the organization, with strategic objectives, OKR, skill sets, budgets, and product requirements differing across the board.
A one-size-fits-all approach with a single data lakehouse is not sufficient. Business domains have evolved beyond monolithic structures, as the concept of centralization fades away, replaced by the rising need for decentralization and federation.
In order to address these evolving needs, it is crucial to adopt macro architecture patterns that enable business units to scale, reduce backlog, optimize resource allocation, and ensure the development of safe and reliable products.
Two prominent macro architecture patterns that emerge in this context are data mesh and hub-spoke, with the flexibility of hybrid approaches. At the micro level, the lakehouse layers are manifested as nodes, further enhancing the effectiveness of the architecture.
While the macro patterns share some similarities, they also have distinctive characteristics.
In the hub-spoke pattern, a central node acts as the hub, orchestrating and governing the sharing of data among multiple edge nodes functioning as spokes.
On the other hand, data mesh takes a different approach, treating data as a product. It combines decentralized ownership, domain-driven design architecture, self-serve platform design, and product thinking.
Unlike the hub-spoke architecture, the data mesh pattern features independent nodes that facilitate data discovery and sharing through a governed process, eliminating the need for a central hub node.
When selecting the right architecture pattern, three key factors come into play: the differences in data discovery, sharing, and the degree of independence between business units.
By adopting macro architecture patterns like data mesh and hub-spoke, tailored to your unique needs, you can revolutionize your data ecosystem, drive innovation, and gain a competitive edge in today's data-driven world.