A hands-on guide to deploying a production-grade document ingestion pipeline using Apache Airflow, FastAPI, Docker Compose, and PostgreSQL. Covers the full runtime setup: spinning up 5 Docker containers, uploading PDFs via a FastAPI endpoint, triggering and monitoring Airflow DAGs, and verifying parsed chunks in PostgreSQL. Includes code for PDF parsing with PyPDF, sliding-window text chunking, SHA-256 content hashing for deduplication, and an idempotent PostgreSQL init script. Also discusses error handling for corrupted files, key design principles (idempotency, observability, reproducibility, data provenance), and the practical limits of Airflow for GPU-heavy ML workloads like embedding generation.
Table of contents
Run an Apache Airflow DAG with Docker Compose and PostgreSQLProject StructurePDF Parsing and Text Chunking Logic for Airflow DAGDocker Orchestration and Runtime for Airflow and FastAPIRunning the Apache Airflow and FastAPI Document Ingestion PipelineTriggering and Monitoring Apache Airflow DAGsVerifying Document and Chunk Data in PostgreSQLError Handling and Failure Recovery in Airflow PipelineDesign Principles: Idempotency, Observability, and Reproducibility in Airflow PipelinesLimits of Apache Airflow for Machine Learning WorkloadsSummary7.7K Impressions