AI is only as good as the data it's fed. Data Engineering is the discipline of building systems that collect, clean, and transport that data at scale.
1The Architect of Intelligence
Data Engineers focus on the plumbing—maintaining pipelines and ensuring data availability.
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// Data Infrastructure: The Foundationlocalhost:3000
localhost:3000/the-role
Execution Output
Status: Running
Result: Success
2The Data Lifecycle
Ingestion, Storage, Processing, and Serving are the four key stages.
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Data_Lifecycle: {
Ingest: [LOGS, DB, API],
Store: [DATA_LAKE, DATA_WAREHOUSE],
Process: [CLEAN, AGGREGATE],
Orchestrate: [AIRFLOW_DAGS]
}localhost:3000
localhost:3000/the-lifecycle
Execution Output
Status: Running
Result: Success
3Medallion Architecture
A standard pattern for organizing data in a lakehouse environment: Bronze, Silver, and Gold.
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[ AI / ML ]
[ DATA_ENG ]
[ DATA_SOURCE ]localhost:3000
localhost:3000/medallion
Execution Output
Status: Running
Result: Success
