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data architecture · analytics · data platform

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.

유형 블록 다이어그램표준 Ogata / standard controls textbook convention엔진 schematex-blockdiagram업데이트됨 2026. 9. 24.
Data Platform Architecture
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요청 내용

“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.”

그렇다면Add a streaming ingestion service before the data lake.Change the analytics warehouse to a lakehouse.
이 도면에 담긴 내용

그 이면의 의사결정을 읽어 보세요.

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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자주 묻는 질문

자주 묻는 질문

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.