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Data Platform Architecture

This data platform architecture gives business systems and product events a shared landing place in a data lake. Quality checks and a data catalog make the stored data easier to trust and find. Curated data then supports an analytics warehouse, dashboards and machine-learning models. The diagram is intentionally high level: it helps a team agree on the movement from source data to consumption before choosing specific products or pipelines.

類型 方塊圖標準 Ogata / standard controls textbook convention引擎 schematex-blockdiagram更新時間 2026/9/24
Data Platform Architecture
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需求

“Create a data platform architecture with CRM and ERP data, product events, a data lake, quality checks, an analytics warehouse, BI dashboards, ML models, and a data catalog.”

接著試試Add a streaming ingestion service before the data lake.Change the analytics warehouse to a lakehouse.
這張圖裡有什麼

看懂背後的決策。

01Source coverage

Include business systems and product events.

02Landing zone

Collect raw data in a lake.

03Quality control

Check data before it supports decisions.

04Consumption

Serve dashboards and models from curated data.

Use it for a high-level data platform proposal or discovery workshop.

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常見問題

常見問題

What is a data platform architecture?

It is a view of the systems that collect, store, govern and serve an organization's data.

What is the role of a data lake?

A data lake provides a central landing and storage layer for data from many source systems.

Why include data quality checks?

They make validation visible before data is used in reports or models.