Implementing a Self-Service Data Platform

"Many data engineering teams spend a lot of their time reacting to ever-increasing requests from users, answering questions such as: Can I get access to this data? Can you add this field? Why is this broken? This shows that the data engineering team has become a bottleneck, preventing the application of data within the organization to deploy new AI and data-driven applications that could unlock business value."

"In recent years, there has been a move towards self-service infrastructure platforms, removing IT and DevOps engineers from being a bottleneck and empowering software engineers to build and deploy their services and APIs with greater autonomy. These platforms not only remove the bottleneck but also provide tools and services that handle the difficult parts of building and deploying software, such as observability, authentication, and autoscaling."

"What if we could apply the same ideas to data by implementing a self-service data platform that allows teams to build, deploy, and manage their own data products without requiring constant data engineering support? It turns out we can. However, to do so, we require a key component, and that is the data contract."

Data engineering teams often face bottlenecks due to increasing user requests, hindering the deployment of AI and data-driven applications. A shift towards self-service infrastructure platforms has empowered software engineers by removing IT and DevOps bottlenecks. This concept can be applied to data through a self-service data platform, enabling teams to build and manage their own data products independently. A crucial element for this platform is the data contract, which contains essential metadata that provides context for the tools and services needed for effective data management.

Read at Medium