Python Automation / Data Engineering
Enterprise Data Extraction & Intelligence Platform
Sole developer — architecture, spider engineering, CLI, storage and export pipeline
A production-grade data extraction and intelligence platform built to discover, traverse, normalize and store structured records from multi-page sources — with an interactive CLI, multiple specialized spiders, dual storage backends and timestamped JSON exports.
53
Root listings discovered
53
Detail pages queued and extracted
1,569
Structured records captured
12
Fields extracted per record
Figures reflect a single demonstrated run. Output volume varies by source size and run configuration.
Platform Overview
From raw web sources to structured, queryable intelligence.
The platform was built to replace manual data collection with a reliable, repeatable extraction pipeline. It discovers root listings, queues detail pages automatically, extracts structured fields from each record, normalizes the output and writes to both a local SQLite database and a production PostgreSQL backend.
An interactive CLI menu drives the entire workflow — operators select the spider, run mode and export format without touching configuration files. Real-time progress logging gives visibility into every stage of the run.
The Challenge
Multi-page sources, inconsistent structure, no public API.
The target sources exposed data across paginated listing pages and individual detail pages, with no API and inconsistent field presence across records. A naive scraper would miss detail pages, silently drop malformed records and produce an unqueryable flat file.
The platform needed to handle pagination automatically, follow detail-page links without duplication, tolerate missing fields gracefully, normalize values before storage and produce both a queryable database and portable JSON exports — all from a single run command.
Extraction Pipeline
End-to-end — from CLI launch to structured export.
CLI Launch
Operator selects spider and run mode. Environment validated before any request is made.
Root Discovery
Spider traverses all paginated listing pages, collecting detail-page URLs into the queue.
Detail Extraction
Each queued URL is fetched and parsed. Fields extracted per the spider's item pipeline.
Normalization
Extracted values normalized — encoding, whitespace, numeric parsing, date standardization.
Storage Write
Valid records written to SQLite (demo) or PostgreSQL (production). Quarantine log updated for failures.
Export & Stats
Timestamped JSON export written. Run statistics printed: pages, records, quarantine count, export path.
Platform Architecture
Purpose-built for reliability at every stage.
Interactive CLI
Python-based menu system — operators select spider, run mode (Demo/Production) and export target without editing config files. Validates environment before any spider starts.
Spider Engine
Multiple specialized Scrapy spiders — each tuned to a specific source structure. Spiders handle pagination, detail-page queuing and field extraction independently.
Pagination & Link Queue
Root spider discovers all listing pages, extracts detail-page URLs and queues them for the detail spider. Deduplication prevents re-processing the same URL across runs.
Field Extraction
Per-spider item pipelines extract structured fields from each detail page. Missing fields are flagged rather than silently dropped — records with critical gaps are quarantined for review.
Normalization Pipeline
Extracted values pass through a normalization stage — whitespace stripped, numeric fields parsed, date formats standardized and encoding issues resolved before any record reaches storage.
Dual Storage Backend
Demo mode writes to a local SQLite database for development and demonstration. Production mode targets PostgreSQL with Celery task queuing for high-volume runs.
JSON Export
Every run produces a timestamped JSON export alongside the database write — portable, diff-able and importable into downstream tools without a database connection.
Consolidated Export
A separate consolidation step merges outputs from multiple spider runs into a single unified export file, deduplicating records by a stable key.
Platform in Action
Real output from a live execution run.
Screenshots show actual platform execution — setup wizard, live spider progress, extracted JSON records and the README feature overview. Click any screenshot to enlarge.
Setup wizard — run mode selection
The CLI setup wizard prompts for run mode before any spider starts. Demo mode initializes a local SQLite database; Production mode targets PostgreSQL. Environment is validated before the menu loads.
Live spider run — 53 listings, 53 detail pages
A live run of the Pe Picior spider. The CLI shows the demo database initializing, the spider discovering 53 root listings, queuing 53 detail pages and extracting records in real time. Log output is visible at every stage.
JSON export — 1,569 structured records
The timestamped JSON export produced at run end. Each of the 1,569 records contains the full field set extracted from the detail page — normalized, typed and ready for downstream processing or import.
README — feature overview and dataset documentation
The README documents the platform's architecture, spider inventory, dataset types and production configuration. Four distinct datasets are supported across three source structures, each handled by a dedicated spider.
Error Recovery & Resilience
Failures are visible and actionable — not silent.
Quarantine on parse failure
Records that fail field extraction are written to a quarantine log with the raw response attached — operators can inspect and reprocess without re-running the full spider.
HTTP retry with backoff
Transient network errors trigger automatic retries with exponential backoff. Persistent failures are logged with the URL and HTTP status so they can be investigated independently.
Duplicate URL detection
The link queue tracks seen URLs across the run. Detail pages discovered more than once are skipped and logged — preventing duplicate records in the output.
Environment validation on startup
The CLI validates database connectivity and required configuration before launching any spider. A misconfigured environment fails fast with a clear error rather than producing a partial run.
Run-level statistics
Each run produces a summary: pages crawled, records extracted, records quarantined, errors encountered and export file path. Operators know exactly what the run produced.
Validation & Normalization
Structured output — not raw strings.
Platform Capabilities
What the platform delivers.
Demonstrated Results
Verified output from a live execution run.
Figures reflect a single demonstrated run. Output volume varies by source size and run configuration.
Technical Capabilities
Outcome
Manual collection replaced with a reliable, repeatable pipeline.
The platform replaced ad-hoc manual data collection with a structured, auditable extraction workflow. Operators run a single CLI command, select the spider and receive a populated database and portable JSON export at the end of the run — with full visibility into what was extracted, what failed and why.
Responsible Use
Built for legitimate data intelligence work.
Confidentiality & Responsible Use
Customer identity, source-specific selectors, proprietary scraping logic and production credentials are not disclosed. Demonstrated figures reflect a single verified run against a non-confidential source used for platform validation.
Data Extraction & Intelligence
Need structured data from sources without an API? I build extraction pipelines for legitimate data intelligence work.