#47 Skipping the Fluff of Domain Driven Design for Data Mesh - Interview w/ Lorenzo Nicora

Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB) https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Transcript for this episode (https://docs.google.com/document/d/1hUFtLxvZf8OMK-ptJP-GEgNcaVi3LGFESb2-4DAiCO0/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova-on-demand/?datameshradio (here) and their great data mesh resource center https://www.starburst.io/info/distributed-data-mesh-resource-center/?datameshradio (here) In this episode, Scott interviewed Lorenzo Nicora, Principal Data Consultant at data mesh and AI focused consultancy Mesh-AI. Scott asked Lorenzo to be on to continue the series of interviews on domain driven design (or DDD) for data. It is a topic that many are struggling with so having lots of perspectives on it is crucial. On the episode title, a key output was explicit permission to skip a lot of the tactical patterns of DDD. Others have also said similar things but I wanted to make sure it was explicit. Before we jump into the DDD parts, Lorenzo made a good point on your data mesh Proof of Concept / starting your journey. You need to start with manageable problems. Start with a consumer-driven problem but a source/producer-aligned data product. There is a lot of nuance in the interview on why this matters. Per Lorenzo, identifying the domains is crucial but it is the hardest part of DDD. That shouldn't scare you because you can start with things being a bit blurry. It's important to understand your high-level domains but you can get moving without mapping out all of your domains. A key theme from Lorenzo: the language is at the center of everything in DDD. It is part of the data modeling and it goes all the way down to the code. Per Lorenzo, DDD is all about communication, knowledge capture, and knowledge sharing. Knowledge capture is about extractin