> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/data/engineering-intro/data-architecture.md). # Data Architecture A platform needs an architecture before the warehouses and pipelines stack up. The page walks through a few data-platform architectures, from a modular stack to multi-petabyte and evolving platforms, and ends with an introduction to domain-driven design for data. The architecture question and the ownership question are the same question seen from two sides. The same notes are in [Data Mesh](/data/engineering-intro/data-mesh.md) and [ML Architecture](/ai-engineering/ml-architecture.md). The first architecture is the smallest one that still holds together: [How to build a modular data stack](https://medium.com/the-prefect-blog/how-to-build-a-modular-data-stack-data-platform-with-prefect-dbt-and-snowflake-89f928974e85) is the modular data-platform write-up. Scale changes the picture. Nielsen shows [a multi-petabyte data platform using EMR](https://www.youtube.com/watch?v=9hUTkBsuIZA), the AWS re:Invent 2020 talk on how they built it on Amazon EMR. A platform is also never finished, and [Evolving and scaling the data platform at Yotpo](https://www.dataengineeringpodcast.com/yotpo-data-platform-architecture-episode-285/) is the podcast episode that treats building a data platform as an iterative, evolutionary process that needs collaboration. Once the platform grows past one team, the boundaries have to follow the business, which is where the [Intro to domain-driven design (DDD)](https://medium.com/latentview-data-services/introduction-to-domain-driven-design-part-1-of-2-6630f72f2853) comes in, the first part of a two-part introduction. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/data/engineering-intro/data-architecture.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.