☁️ SaaS & Cloud · Data Flow
Data Lakehouse Pipeline
How data from apps, databases and events lands in a lakehouse and becomes dashboards and ML features: bronze, silver and gold layers.
Drawing diagram…
What this diagram shows
- Change data capture streams database changes without heavy queries
- Bronze keeps raw data, silver cleans it, gold models it for reporting
- BI and ML both read from the same governed tables
Prompt used
Data flow for a lakehouse: operational databases stream changes through CDC (Debezium) and app events through Kafka, SaaS tools are loaded by a batch connector (Fivetran). Everything lands raw in the bronze layer (Delta Lake on object storage), Spark jobs clean and de-duplicate into silver, then build business models (orders, customers, revenue) in gold. BI dashboards and ML feature pipelines read gold; a data catalog tracks lineage and access.
Mermaid code
flowchart LR DB[(App Databases)] -->|CDC - Debezium| K[Kafka] EV[App Events] --> K SAAS[SaaS Tools: CRM, ads] -->|Batch connector| BR K --> BR[(Bronze - raw)] BR -->|Clean, de-duplicate| SI[(Silver - cleaned)] SI -->|Business models| GO[(Gold - orders, customers, revenue)] GO --> BI[BI Dashboards] GO --> ML[ML Feature Pipelines] CAT[Data Catalog] -.lineage and access.- SI CAT -.- GO
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