DataPulse ETL

High-Throughput Financial Data Extraction & Validation Engine

ClientInstitutional Quantitative Fund

Stack & DisciplineDATA PIPELINES • PYDANTIC

Year2024

https://production.llm-data-pipeline.cloud/workspace

The Challenge

“Financial tables varied wildly across 500+ public companies, causing brittle legacy regex extractors to corrupt fundamental analyst models.”

An institutional quantitative fund needed to extract non-standard financial footnotes, segment earnings, and executive compensation numbers from SEC EDGAR filings.

Legacy OCR and regex scripts had an error rate of over 14% on non-standardized multi-column balance sheets and footnote adjustments.

Human data entry teams took 48 hours to clean and reconcile quarterly earnings data, causing analysts to miss real-time trading alpha.

Legacy System Bottlenecks

14% Extraction Error Rate: Corrupted fundamental valuation multiples.

48-Hour Ingestion Delay: Quantitative models traded on stale balance sheet assumptions.

Footnote Blindness: Critical litigation risks and debt covenants were ignored.

System Topology

High-Throughput Async Extraction & Schema Validation Pipeline

A distributed Celery worker cluster streaming SEC filings through layout-aware vision models, Pydantic strict schemas, and automated mathematical balance checks.

Compiling runtime graph schematic...

Layer 01FastAPI / Redis

Async Ingestion Gateway

Listens to SEC EDGAR feeds and streams incoming HTML/XBRL filings in parallel.

Layer 02Pydantic v2

Pydantic Validation Node

Verifies that Total Assets == Total Liabilities + Equity with 100% mathematical consistency.

Layer 03PostgreSQL 16

PostgreSQL Storage Engine

Normalized relational schemas ready for instant microsecond SQL quant queries.

Product Workbenches

Live Interface & Inspection Workflows

Real-Time SEC Filing Extraction Workbench

Real-time SEC filing parser and reconciler displaying source table extraction and validated JSON schemas.

12s Median Ingestion SpeedPydantic Strict SchemasAutomated Footnote Cross-Checking

P&L Variance & Distribution Analytics

Telemetry dashboard tracking extraction throughput, anomaly distribution, and quant model ingestion feeds.

50,000+ Daily Filings99.98% Schema AccuracyZero Downstream Quant Failures

System Decisions

Architectural Trade-Offs

Pydantic v2 Strict Mode Over Dynamic Type Coercion

Superseded: Loose JSON Schema Validation • Raw Python Dict Parsing

Financial quant models cannot tolerate coerced float approximations. Strict Pydantic parsing rejects ambiguous strings and forces deterministic float precision.

Celery Distributed Workers Over Serverless Lambda

Superseded: AWS Lambda Functions • Synchronous HTTP Worker Threads

Processing 200-page 10-Ks can take 15 seconds of steady compute. Dedicated worker pools prevented Lambda cold-start latency and timeout limitations.

Impact

Operational Telemetry

SCHEMA ACCURACY99.98%

Pydantic-validated JSON extraction schemas

PROCESSING SPEED12s / 10-K

200-page complex filing extraction

DAILY VOLUME50k+ Docs

High-throughput Celery/FastAPI worker pool

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