02Systems2024

ETL Pipeline for Binary Data Files

A large-scale pipeline that turns court-judgment PDFs into a clean, retrieval-ready dataset.

Focus

Ingestion, normalization, enrichment

Scale

~400k judgment PDFs

Stack

Python · Requests · BeautifulSoup · PostgreSQL · Docker

Sources

Multiple courts + legal sites

~0Kdocuments ingested

Resumableevery stage retryable

0duplicate records

Problem

A pile of binaries.

Court judgments are published as PDFs with inconsistent formatting, incomplete metadata, and uneven text quality. The core challenge is turning a large pile of binary files into a consistent, structured dataset that is reliable for search and downstream automation.

›Multiple sources with different page templates, naming, and metadata conventions.
›Binary PDF ingestion where extraction quality varies and edge cases are common.
›Downstream retrieval needs stable identifiers, structured fields, and searchable text.

Approach

Deterministic IDs, clear stages, explicit validation.

›Scrape judgment links + metadata from multiple courts and legal sources.
›Download PDFs with retry and deduplication, storing a stable content fingerprint.
›Extract text and normalize formatting into a clean canonical representation.
›Enrich records with structured fields and headnotes/summaries where applicable.
›Load into a relational schema optimized for retrieval and filtering.

Operational notes

Resumable ingestion: every stage can be retried without duplicating records. Validation checkpoints run continuously, and raw artifacts stay cleanly separated from normalized and enriched outputs.