Have you built any data pipeline for processing PDF documents?

Building a PDF data pipeline starts with ingestion: upload PDFs to an S3 bucket or stream them via Kinesis. Next, extraction can be done with Amazon Textract for OCR and structured data, or open‑source libraries like PDFMiner for text. The extracted text is then transformed—cleaned, tokenized, and enriched with metadata—using AWS Glue or Spark. For large volumes, Glue ETL jobs can run in parallel across partitions. The transformed data is stored in a query‑friendly format such as Parquet in S3 or loaded into Amazon Redshift Spectrum for analytics. Orchestration is handled by AWS Step Functions or Apache Airflow, ensuring retries and monitoring. If you need real‑time indexing, you can push the text into OpenSearch for full‑text search. This architecture scales horizontally, handles OCR errors, and keeps the pipeline modular so you can swap extraction engines or storage backends without rewriting the entire flow.