#34 Tackling Challenges Together at Talkdesk: An Early Journey Story – Interview w/ José Cabeda

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/1Ei1x3pCjrrbBzrmTmCr3a3c7fWUYuQAzSFYutLVuY-E/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova?datameshradio (here) (info gated) In this episode, Scott interviewed José Cabeda, Data Engineer at Call-center-as-a-service provider Talkdesk. They talked about Talkdesk's start to their data mesh journey and progress so far. When José came across Zhamak's original post, it spoke to a number of the challenges Talkdesk was facing, checking many of the boxes to where they wanted to head. The team started from a single data product and iterated from there. While they are still relatively early in their journey, like every company, they have advanced far past their initial use case. At Talkdesk, a data product is typically a single table or view in Snowflake but the company's North Star is event streaming as their key information storage and sharing mechanism. However, it was sometimes difficult to train people to understand the difference between a business event - something that occurred in the real world - and an event streaming event. José had a few key takeaways and recommendations for those implementing data mesh: 1. Change will be constant in a data mesh implementation so it is best to standardize the way people and systems will interact as much as possible. Define expectations! 2. Be open to new ideas, there are many challenges ahead so it's best to face them together. 3. Use a single universal ID for major concepts like account or business events to make interoperability easier / possible. 4. Don't be afraid to slice your data in different ways to serve different use cases. 5. To drive buy-in, start with a single use case, whether that is a data product or multiple data products - most people recommend 2-3 data products in your Po