#90 Sharing Data Reliably in Hyperscale Mode - Interview w/ Björn Smedman

https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB) Transcript for this episode (https://docs.google.com/document/d/1C8KjRoqwiwoZaSQ9LcuZ4Xew_TCx1A69VnEOIeZkLp0/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 Björn Smedman, Engineering Manager at Communication Platform-as-a-Service (CPaaS) company Sinch. Some interesting thoughts or takeaways: A good indicator for when decentralizing your data team might make sense is the cognitive load of a centralized data team. How many systems - including a measure of how complex - are they managing? How much of their time is spent in meetings, especially trying to understand context/requests? Is there starting to be combative prioritization from multiple domains? It can be very beneficial and scalable to apply data mesh principles to non analytical use cases, especially sharing data for application purposes. It is still often difficult to prioritize creating a data product for machine learning without knowing the business value of the ML model. But the ML team needs the data first before they can figure out the business value of the ML model. You have to make speculative bets. If you see the data platform team start to dig into the semantics of a use case, that's a red flag that people are trying to leverage them as a data team. And while you want a centralized data platform team, you probably