Description
In this training course, we will show you what the four principles of Data Mesh mean. You will learn about the challenges of introducing data mesh and receive recommendations for a step-by-step approach. Together we will design a data product, the central element in a data mesh, using our Data Product Canvas and show you the implementation alternatives. At the end of the workshop, you will be able to evaluate the socio-technical implications of Data Mesh and design data products.
The Data Mesh concept is based on domain-oriented, decentralized data architectures and enables development teams to perform data analysis autonomously. Data Mesh is a socio-technical data architecture and is presented in the form of the following four principles:
The “domain ownership” principle assumes that domain teams take responsibility for their data. According to this principle, analytical data should be structured in domains, similar to the team boundaries that correspond to the bounded contexts. Responsibility for analytical and operational data is transferred from the central data team to the domain teams.
The principle of “data as a product” applies the philosophy of product thinking to 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 as data products. Basically, the domain data should be treated like any other public API.
The third principle is to apply the “platform thinking” idea to the data infrastructure. A dedicated data platform team provides domain agnostic functions, tools and systems for the creation and consumption of interoperable data products for all domains.
The principle of “Federated Computational Governance” represents cross-organizational processes for data governance. This principle achieves the interoperability of all data products through standardization determined by the governance guild. The main objective is to comply with the organizational rules and regulations of the industry.
Agenda
The motivation behind Data Mesh. What are typical problems in data engineering that lead to the decentralization of data architectures?
When is data mesh the right approach?
The principle of “domain ownership”
The principle of “data as a product”
The “self-serve data platform” principle
The principle of federated computational governance
Design of a data product
Your Benefits
Learn the difference between operational and analytical data.
Learn the key data mesh principles such as domain ownership, data as a product, self-serve data platform and federated computational governance.
Learn how to design a data product.
Learn about the interplay between multiple data products in a data mesh
Learn about the importance of socio-technical aspects within a data mesh.
Audience
Software architects, data experts
Training Objectives
Understand data mesh concepts for decentralized data architectures
Understand the design and implementation of data products
Understand the four data mesh principles
Be able to define technical and socio-technical components for data mesh
Your Trainers
Dr. Larysa Visengeriyeva
INNOQ
Machine Learning and MLOps
- AI Products with Domain-driven Design
- Data Mesh: Introduction
Larysa is a senior consultant with INNOQ in Berlin. She received her doctorate in Augmented Data Quality Management at the TU Berlin. At INNOQ she is working on the operationalization of Machine Learning (MLOps). She’s the author of ml-ops.org.
Dr. Simon Harrer
INNOQ
Passende Architektur, Clean Code, Remote Mob Programming
- Data Mesh for Managers
- Data Mesh: Introduction
- Online Team Event with Remote Mob Programming
Dr. Simon Harrer is a Senior Consultant at INNOQ. He is a software developer at heart who has now turned to the dark side, namely the world of data. He co-authored datamesh-architecture.com and translated the Data Mesh book by Zhamak Dehghani into German. He is currently developing the Data Mesh Manager, a SaaS product to fast-track any data mesh initiative.
Jochen Christ
INNOQ
Self-contained Systems, Autor von rest-feeds.org
- Data Mesh for Managers
- Data Mesh: Introduction
- Online Team Event with Remote Mob Programming
Jochen Christ is a Senior Consultant at INNOQ. He is an experienced software architect and Data Mesh specialist. He has supported over 10 companies in the introduction of Data Mesh. Jochen is co-author of datamesh-architecture.com, datamesh-governance, and datacontract.com.
Theo Pack
INNOQ
Software-Architektur, verteilte Systeme, Cloud-Native
- Data Mesh: Introduction
Theo ist Senior Consultant bei INNOQ und seit 10+ in der Softwareentwicklung tätig. Er begeistert sich für Cloud-native Anwendungen, verteilte Systeme, Domain-driven Design, DevOps und agile Softwareentwicklung.
Technical Information and Books
Data Mesh
Wir befinden uns an einem Wendepunkt im Umgang mit Daten. Unser bisheriges Datenmanagement wird den komplexen Organisationsstrukturen, den immer zahlreicheren Datenquellen und dem zunehmenden Einsatz von KI nicht mehr gerecht. Dieses praxisorientierte Buch von Zhamak Dehghani führt dich in Data Mesh ein, ein dezentrales soziotechnisches Konzept basierend auf modernen verteilten Architekturen. Data Mesh ist ein neuer Ansatz für die Beschaffung, Bereitstellung, den Zugriff und die Verwaltung analytischer Daten, der auch skaliert. Ins Deutsche übersetzt von unseren Trainern Jochen Christ und Simon Harrer.
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