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In this podcast, Daniel Bryant sat down with Zhamak Dehghani, principal consultant, member of technical advisory board, and portfolio director at ThoughtWorks. Topics discussed included: the motivations for becoming a data-driven organization; the challenges of adapting legacy data platforms and ETL jobs; and how to design and build the next generation of data platforms using ideas from domain-driven design and product thinking, and modern platform principles such as self-service workflows.

Why listen to this podcast:

 - Becoming a data-driven organization remains one of the top strategic goals of many organizations. Being able to rapidly run experiments and efficiently analyse the resulting data can provide a competitive advantage.
 - There are several “architecture failure modes” within existing enterprise data platforms. They are centralized and monolithic. The composition of data pipelines are often highly-coupled, meaning that a change to the data format will require a cascade of changes throughout the pipeline. And finally, the ownership of data platforms is often siloed and hyper-specialized.
 - The next generation of enterprise data platform architecture requires a paradigm shift towards ubiquitous data with a distributed data mesh.
Instead of flowing the data from domains into a centrally owned data lake or platform, domains need to host and serve their domain datasets in an easily consumable way.
 - Domain data teams must apply product thinking to the datasets that they provide; considering their data assets as their products, and the rest of the organization's data scientists, ML and data engineers as their customers.
The key to building the data infrastructure as a platform is (a) to not include any domain specific concepts or business logic, keeping it domain agnostic, and (b) make sure the platform hides all the underlying complexity and provides the data infrastructure components in a self-service manner.


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Emil Woźniak