Data Meshes.pptx
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A data mesh is a decentralized approach to data management that aims to democratize access to data across an organization. It moves away from a centralized model towards distributed data product teams that each own specific domains. Implementing a data mesh requires establishing technological, procedural, and political foundations like interoperability standards, shifting responsibilities away from central teams, and increasing data literacy. While a data mesh can provide benefits, it is a significant change that requires overcoming political and process challenges, and may not be a good fit for all organizations.
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Data Meshes.pptx
- 1. Data Meshes A brief introduction and field report
- 2. Agenda 1. Who am I? 2. What is a data mesh(DM) a. Why b. What c. How 3. When should it be used (or not) 4. Experience a. Personal observations deploying DMs b. What does the community say 5. Resources and further reading
- 4. Timeline 1999 MCSE - Y2K Support 2009 SQL Server DBA 2020 BI Developer, Manager and Data Engineer Today Senior Data Engineer Massive Nerd
- 5. Why?
- 6. The democratization of data is one of the biggest competitive advantages companies can achieve ~ Joyce Kay Avila
- 7. In the real world, that’s easier said than done. A centralized IT bottleneck for data production is more or less the norm
- 9. What?
- 12. How?
- 13. Requirements - data mesh (before you even get “started”) ● Technological ○ Establish a level of decoupling from the underlying technology, i.e. an interoperability plane ● Procedural ○ Move data management responsibilities away from the central product team ● Political ○ Remove the customer-supplier relationship between business and IT. I.E. establish Domain Teams. ○ Establish a higher degree of data literacy in the organization ● Data Product Teams: ○ Typical questions might be - Where do we start? How do we do this ■ Data platform team needs to create templates that embed best practices ■ Think of a MVP
- 14. How to implement data product ● Create a guardrail, sample data product that incorporate the complete lifecycle, i.e. CI/CD ● Within the CI/CD, create computational policies, for example, to check for compliance as far as encryption, POPI etc. ● Policies are defined by the federated governance and applied in the localized data product context ● It’s important to remember that the data product team is responsible for defining the data contract, such as tests and fields needed
- 17. Use the template
- 20. Should I use a data mesh?
- 22. Experience
- 26. My own experience ● Companies tend to jump onto the data mesh bandwagon without cleary understanding the amount of work and culture shift required ● Companies belief they have a data mesh when they still maintain a centralized data engineering team, responsible for everything ● Residual, difficult-to-change politics and process can get in the way of affecting a real change, a democratization of the data ● Data mesh is not a silver bullet ● It’s still early days and there are some success stories
- 27. Resources
- 35. The end SELECT * FROM thank_you;
Editor's Notes
- The domain ownership principle mandates the domain teams to take responsibility for their data. According to this principle, analytical data should be composed around domains, similar to the team boundaries aligning with the system’s bounded context. Following the domain-driven distributed architecture, analytical and operational data ownership is moved to the domain teams, away from the central data team. The data as a product principle projects a product thinking philosophy onto analytical data. This principle means that there are consumers for the data beyond the domain. The domain team is responsible for satisfying the needs of other domains by providing high-quality data. Basically, domain data should be treated as any other public API. The idea behind the self-serve data infrastructure platform is to adopt platform thinking to data infrastructure. A dedicated data platform team provides domain-agnostic functionality, tools, and systems to build, execute, and maintain interoperable data products for all domains. With its platform, the data platform team enables domain teams to seamlessly consume and create data products. The federated governance principle achieves interoperability of all data products through standardization, which is promoted through the whole data mesh by the governance guild. The main goal of federated governance is to create a data ecosystem with adherence to the organizational rules and industry regulations.
- Source Data engineering Podcast
- The underlying theme is that the data product team shouldn’t create a burden of work or responsibility for other teams. No downstream impact