AI Data Pipelines and Feature Engineering at Scale: Modern Approaches 2025
Feast feature store, dbt transformations, and streaming features with Kafka
AI Data Pipelines and Feature Engineering at Scale: Modern Approaches 2025
Feast feature store, dbt transformations, and streaming features with Kafka
Build production-grade data pipelines for ML with Apache Spark, dbt, Feast feature store, and streaming data from Kafka. Learn feature engineering patterns that improve model performance.
Production ML requires robust data pipelines and feature engineering infrastructure. Key components: 1) Data ingestion with Kafka for streaming and batch extraction via Airbyte/Fivetran. 2) Transformation layer using dbt for SQL-based feature computation with testing. 3) Feature store (Feast/Tecton/Hopsworks) to serve features consistently in training and serving - eliminates training-serving skew. 4) Feature engineering patterns: lag features, ratio features, interaction features. 5) Data quality with Great Expectations for automated validation, anomaly detection for distribution drift. 6) Streaming features with Apache Flink for real-time computation. Feature stores reduce ML iteration time by 60% by reusing precomputed features across teams.
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