Summary of Lessons from the trenches in Data Mesh – Zhamak Dehghani and Sina Jahan

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00:00:00 - 01:00:00

In the "Lessons from the trenches in Data Mesh" video, Zhamak Dehghani and Sina Jahan discuss the various aspects of data mesh, emphasizing the importance of standardizing how data is described in order to make it easier to consume. They also discuss the need for a product owner for data as a product, the importance of context mapping, and the need for a global identifier system.

00:00:00 This company was struggling to manage its data, as it had multiple copies of its data warehouse across different locations. Zhamak Dehghani and Sina Jahan from Data Mesh were hired to help them adopt a data mesh architecture to help them manage and analyze their data more effectively.
00:05:00 The speaker discusses how an organization successfully transitioned to a modern data stack, using COVID as an example. The context of the organization's data was extremely complex, with a number of domains, systems, and teams. The speaker explains that while it was possible to move the data to the cloud, it was not always successful and there were diminishing returns. They share a case study of how COVID helped the organization reduce costs associated with cancer treatment.
00:10:00 The speaker discusses the reasons why data mesh was chosen to be implemented at a particular organization, and goes on to explain how the approach was different from traditional data warehousing approaches. The speaker also discusses the benefits of decentralizing data management around domains, and how this can reduce complexity for users.
00:15:00 The four principles of a data mesh outlined by Zhamak Dehghani and Sina Jahan are decentralized domain ownership, organization-focused effort, self-service platform, and federated computational governance. These concepts are introduced on a theoretical level and then implemented in a practical example for each. The first principle, decentralized domain ownership, shift the responsibility for analytical data from a centralized team to the business units that create and produce the data. The second principle, organization-focused effort, involves collaboration between different teams in an organization to provide the analytical data for the business. The third principle, self-service platform, allows the business units to access and use the data themselves without having to rely on a centralized provider. The fourth principle, federated computational governance, embeds the governance of the data mesh within the self-service platform itself to enable it to go to production.
00:20:00 The video discusses the challenges of data mesh transformation, focusing on the need to remove data teams from the process and enable domain teams to manage data products. It notes that while this is a difficult task, it is essential in achieving the goal of data ownership by the domains.
00:25:00 This video discusses the transition from a traditionally data officer role of owning and serving data to a role of enabling data ownership and decentralized data management. The chief data officer becomes an enabler, not an owner, of data, and the role of chief data analytics officer is created to centrally manage the data platform. The product owner role is introduced toalign data products with consumers and to identify new domains for data consumption.
00:30:00 The speaker discusses how data mesh helps organizations build data products that are more aligned with consumption scenarios and where consumers take ownership of creating them. This movement will become more clear as the presentation progresses.
00:35:00 Zhamak Dehghani and Sina Jahan discuss the various aspects of data mesh, emphasizing the importance of standardizing how data is described in order to make it easier to consume. They discuss the use of a domain-specific language to describe data, and the importance of policies and code to manage and change the data. Finally, they discuss the need for developers to have access to data in a way that is conformant with the needs of their specific data product.
00:40:00 The author describes how data products are autonomous units that are independent from other data products, and that to make this autonomy possible, they need to think about the level of abstraction of the data. They go on to say that this is not easy to do, and that there are cost constraints to this.
00:45:00 The video discusses the need for a product owner for data as a product, the importance of context mapping, and the need for a global identifier system.
00:50:00 The video discusses the implementation of a data mesh model by Zhamak Dehghani and Sina Jahan. The model registry is used to define and connect different models of data. The self-service platform is used to reduce duplicate work and make it easier for customers to use data.
00:55:00 The video discusses the need for data discovery and how to access data products for analysis. It also discusses how to use various data quality and observability tools to ensure the health of data products. The presenter suggests using Azure logs to create dashboards of data product health.

01:00:00 - 01:20:00

This video discusses the lessons learned from building a data mesh platform. Zhamak Dehghani and Sina Jahan talk about the challenges they faced in balancing the needs of data product developers and consumers, and how Spark transformed their platform from a collection of scripts into a declarative platform users can understand and use. They also discuss the challenges of embedding analytics into data mesh transformations and how the platform evolved to allow for more flexibility. Finally, they talk about how the governance rules for a data mesh platform became embedded as a part of the self-service platform, and how Azure handled sensitive data.