Data Engineering Podcast Revisiting The Technical And Social Benefits Of The Data Mesh
01:10:53
Prioritize Analytical Use Cases
- Embed analytical use cases as a first-class priority in application design.
- Make data a core application development responsibility, not an afterthought.
Bridging the Divide
- Different data technologies exist for transactional and analytical purposes.
- Data mesh respects these differences by utilizing specialized data stores.
Frameworks for Analytics
- Software frameworks like Django simplify web development by handling common tasks.
- Similar frameworks focusing on analytical output could ease data mesh adoption for developers.
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Introduction
00:00 • 2min
The Data Mesh - A Brief Introduction
02:06 • 2min
The Challenges of Complexity and Data Management
04:10 • 5min
The Four Principles of Scaled Solutions
08:53 • 4min
The Rise of the Modern Data Stack
12:35 • 4min
Data Analytics and Application Development - Is There a Need to Connect It?
16:49 • 2min
Imbedded Analytical Solutions - The Foundational Need
19:03 • 1min
Data Base Innovation
20:17 • 5min
The Integration Between Data Moding and the Source Is Already There
24:48 • 2min
Data Fold - Data Engineering Podcast Dot Com
26:59 • 2min
Application Development Frameworks - Data Mesh
29:22 • 2min
Data Product Creation
31:35 • 1min
Data Mesh - A New Approach to Data Mining
32:58 • 3min
Datamish
36:23 • 3min
Data Quantum - The Building Block of the Mesh
38:54 • 5min
The Hard Engineering Disciplines
44:05 • 2min
Using a Data Catalogue, Is It a Good Idea?
45:37 • 3min
Data Mesh
48:49 • 4min
The Evolution of the Data Mesh
53:08 • 2min
The Impact of Data Mesh in the Application Development Cycle
54:54 • 3min
Optimization of the User Experience
57:47 • 3min
What Are Some of the Most Unexpected or Challengesome Lessons That You've Learned?
01:00:39 • 3min
Data Meshes the Wrong Approach to Day
01:03:13 • 3min
Data Mesh and the Community?
01:05:47 • 2min
Data Strategy and Execution - Part 4
01:08:10 • 2min
Summary
The data mesh is a thesis that was presented to address the technical and organizational challenges that businesses face in managing their analytical workflows at scale. Zhamak Dehghani introduced the concepts behind this architectural patterns in 2019, and since then it has been gaining popularity with many companies adopting some version of it in their systems. In this episode Zhamak re-joins the show to discuss the real world benefits that have been seen, the lessons that she has learned while working with her clients and the community, and her vision for the future of the data mesh.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m welcoming back Zhamak Dehghani to talk about her work on the data mesh book and the lessons learned over the past 2 years
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by giving a brief recap of the principles of the data mesh and the story behind it?
- How has your view of the principles of the data mesh changed since our conversation in July of 2019?
- What are some of the ways that your work on the data mesh book influenced your thinking on the practical elements of implementing a data mesh?
- What do you view as the as-yet-unknown elements of the technical and social design constructs that are needed for a sustainable data mesh implementation?
- In the opening of your book you state that "Data Mesh is a new approach in sourcing, managing, and accessing data for analytical use cases at scale". As with everything, scale is subjective, but what are some of the heuristics that you rely on for determining when a data mesh is an appropriate solution?
- What are some of the ways that data mesh concepts manifest at the boundaries of organizations?
- While the idea of federated access to data product quanta reduces the amount of coordination necessary at the organizational level, it raises the spectre of more complex logic required for consumers of multiple quanta. How can data mesh implementations mitigate the impact of this problem?
- What are some of the technical components that you have found to be best suited to the implementation of data elements within a mesh?
- What are the technological components that are still missing for a mesh-native data platform?
- How should an organization that wishes to implement a mesh style architecture think about the roles and skills that they will need on staff?
- How can vendors factor into the solution?
- What is the role of application developers in a data mesh ecosystem and how do they need to change their thinking around the interfaces that they provide in their products?
- What are the most interesting, innovative, or unexpected ways that you have seen data mesh principles used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on data mesh implementations?
- When is a data mesh the wrong approach?
- What do you think the future of the data mesh will look like?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Data Engineering Podcast Data Mesh Interview
- Data Mesh Book
- Thoughtworks
- Expert Systems
- OpenLineage
- Data Mesh Learning
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA