Now, as we’ve seen what GraphQL can do, we talk about building an API that leads to the next level of data engineering, which I call Analytics API in this article. The API will empower all stakeholders to use one single source of accessing analytics data in a consistent and decoupled semantic way (and if you know a better name, please let me know!).

The Analytics API architecture with the single endpoint with GraphQL

The Analytics API consists of five main components where GraphQL is the natural fit for the gateway API and the query interface. Besides that, the SQL Connector connects legacy or traditional BI systems that talk SQL natively. The metrics or business logic, also called Metrics Store or Headless BI stored in a Metrics Store.

Suppose you’re in a large organization with a lot of variety. In that case, it’s helpful to have a data catalog that helps discover your data and add owners, comments, ratings, and others to the datasets to navigate between them. The orchestrator updates your content in the data stores consistently and reliably. More about each component a bit later.

Nowadays, this is called a Semantic Layer. Read more on Building an Analytics API with GraphQL: The Next Level of Data Engineering?.

Origin: Building an Analytics API with GraphQL: The Next Level of Data Engineering? | ssp.sh
References: Metrics Layer
Created 2022-02-19