DATA ARCHITECTURE
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.
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CASE ANALYSIS
Scenario
Explaining how operational data becomes analytics and models.
Key decisions
- Source coverage: Include business systems and product events.
- Landing zone: Collect raw data in a lake.
- Quality control: Check data before it supports decisions.
- Consumption: Serve dashboards and models from curated data.
When to reuse this
Use it for a high-level data platform proposal or discovery workshop.
FAQ
Frequently asked questions
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.
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blockdiagram-data-platform-architecture