How I built a streaming pipeline that uses LLMs as a transform layer and Delta Lake for stateful content versioning My first portfolio project (Ballistics) was batch — API calls on a schedule, Airflow orchestration, S3 landing zone. My second (Pulse) was streaming — Kafka, exactly-once delivery, session analytics in dbt. Both used the same transformation tool (dbt) with different ingestion patterns. Sentinel is the third project, and the question changed. Ballistics and Pulse processed structured data — JSON from APIs, simulated clickstream events. What happens when the raw data is unstructured ? When the "transformation" isn't a SQL model but an LLM that extracts entities, sentiment, and summaries from raw HTML? Sentinel is a news intelligence pipeline that ingests articles from multiple sources, uses LLMs to extract structured data, and serves it through an API and dashboard. It's not a product — it's a proof of work for AI-augmented data engineering patterns.…