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MLOps & Infrastructure · intermediate · concept 202 of 311 · Updated Nov 4, 2026

Data Pipelines & ETL

Automated workflows for collecting, cleaning, transforming, and loading data for ML. The unsexy but critical infrastructure that makes everything else possible. At big-data scale the same jobs run on engines like Spark over data lakes, while SQL over relational databases remains where most structured training data actually lives.

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Key terms

ETLApache AirflowdbtData warehouseFeature storeApache Spark & data lakesSQL & relational databases

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