Large enterprises face significant data challenges due to distributed ownership, complex legacy systems, and pervasive data silos.
Centralized data warehouses proved too expensive and inflexible, leading to the rise of data lakes which introduced new problems with governance and complexity.
The data mesh is an organizational and cultural blueprint for data handling, not a specific software or platform you can install.
Traditional data strategies often fail by focusing on ETL pipelines and monolithic platforms instead of solving actual business problems.
Data mesh aligns data architecture with business domains using microservices principles, ensuring solutions are simple and tailored to specific needs.
Adopting a self-serve platform design using public cloud services allows teams to focus on solving data problems instead of managing infrastructure.
The data-as-a-product principle holds domain teams responsible for the quality, availability, and accessibility of their data for others to consume.
A data product must be discoverable, addressable via APIs, trustworthy, self-describing with metadata, interoperable, and secure.
While not a silver bullet, the data mesh framework provides a more effective approach for managing data in large, complex organizations.
45:58Andreas Christian
29:25Matthias Niehoff
43:47Dubravko Dolic & Hüdaverdi Cakir
38:36Ferd Scheepers
50:06Jens Happe
43:13Carola Lilienthal
42:16Adnan Rahic
19:44Dominik Schneider
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Jobs that call for the skills explored in this talk.
Westhouse Consulting GmbH
evoila Frankfurt GmbH
Mainz, Germany
