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On the latest episode of the Data & AI podcast I joined Deepak and Dan as we demystified data contracts.
When building a data platform I’m often thinking about how to reduce friction for my users.
Many data platforms start with a change data capture (CDC) service to extract data from an organisations transactional databases - the source of truth for their most valuable data.
There is strong alignment between the goals of data mesh and data contracts.
I enjoyed this post by Ryan Collingwood on what the 🤬 is a Data Contract?
Data evolves over time, and as such a data contract needs to support that.
While data products are for people, they still require an interface that allows people to access them - usually through a system such as a database, an analytics tool, a notebook, and so on.
Data is at the heart of every meaningful service, and it’s the effective use of that data which builds a product, and a business.
𝗧𝗵𝗲 𝟭-𝟭𝟬-𝟭𝟬𝟬 𝗿𝘂𝗹𝗲 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 One of my favourite talks at Big Data LDN was by Hannah Davies on The Building Blocks of Data Culture During Transformation.
My post the other day on understanding the concepts, and applying them at your organisation reminded me of how I first encountered data mesh, and how I started applying some of the concepts to my organisation.