Real-Time AI Feature Engineering with Spark Structured Streaming and Databricks

<p><em>Part 3 of 5 in the series: When Your AI Pipeline Grows Up</em></p> <p>In the previous post, we worked through the pipeline architecture that gets features from raw events

<p>Raw data doesn't win model competitions. Features do. And when your raw data is tens of billions of rows sitting across multiple sources, you can't afford to run pandas in a

<p>PostgreSQL → DuckDB → SQL Engine → Parquet →And beyond ....</p> <p><strong>A Local-First Analytics Pipeline</strong></p> <p>Data teams often spend more time operating

<p>Every data pipeline makes one foundational decision before a single line of code is written.</p> <p>Does it process data in scheduled chunks, or does it process data as events

<p>Most standard data pipelines run on a schedule. You use tools like Airflow and dbt to extract and transform large batches of API data once a day. However, what would happen if