Data Extraction Intelligence Platform
From scattered, unstructured sources to validated, decision-ready data pipelines your team can rely on.
A modular Python ETL toolkit for extracting data from websites, documents, APIs, and databases—then cleaning, validating, and exporting results in the formats your team actually uses.
Python 3.8–3.12 • CLI + Web UI • Built-in exporters • Anti-bot tooling
Extract from anywhere
Web scraping (JS rendering), PDFs, Excel/CSV, DOCX, SQL/NoSQL, WebSocket streams, RSS/Atom feeds, and service APIs—under one toolkit.
Transform with a pipeline
Chainable cleaning and transformation steps, pattern extraction (email/phone/address), schema validation, and data quality checks.
Export in the formats you need
CSV, Excel, JSON, Parquet, SQLite, Pickle, and HTML—with multi-format export support.
What you get
Enhanced Web Scraping
Pagination, retries, rate limiting, infinite scroll, optional browser automation for JS-heavy sites.
Anti-Bot Stack
Proxy rotation, header/user-agent rotation, stealth browser support, CAPTCHA integrations.
Document Extraction
PDF text extraction plus spreadsheet (Excel/CSV) and DOCX ingestion.
Database Connectors
SQL (SQLite/MySQL/PostgreSQL/MSSQL via SQLAlchemy) + NoSQL (MongoDB/Redis).
API Connectors
Common platforms + GraphQL + rate limiting, retries, auth patterns.
Processing Engine
Cleaners, filters, type conversion, deduplication, validators, and pattern recognition.
Export System
Single exporter or MultiExporter to ship multiple formats in one run.
Scheduling + Ops
Cron-based job scheduling, structured logging, notifications (email/webhook/Slack).
Interfaces
CLI entry point plus a Flask web UI for job/config/result management.
A consistent ETL workflow
Every job follows the same flow, so teams can standardize how data is collected and delivered.
Extract
Choose a connector (web, doc, API, SQL/NoSQL, feeds).
Transform
Apply chainable cleaning and transformation steps.
Validate
Enforce schema and run data quality checks.
Load / Export
Push to storage or export to the formats you need.
Built for real workflows
E-commerce & catalog monitoring
Track product listings, prices, inventory, and changes—supporting pagination and infinite scroll.
Document-driven extraction
Turn PDFs, spreadsheets, and DOCX files into structured, searchable datasets for analytics.
Data warehousing feeds
Run scheduled pulls from APIs/DBs, validate schema, and export Parquet/CSV for downstream pipelines.
Sales/marketing enrichment
Extract patterns (emails/phones/addresses), normalize fields, dedupe, and deliver clean exports.
Technical snapshot
datascraper
Frequently Asked Questions
What data sources does Data Scraper support?
Websites (including JS-rendered pages), PDFs, Excel/CSV spreadsheets, DOCX files, SQL databases (SQLite, MySQL, PostgreSQL, MSSQL), NoSQL stores (MongoDB, Redis), REST/GraphQL APIs, WebSocket streams, and RSS/Atom feeds.
Does it handle anti-bot protections?
Yes. The toolkit includes proxy rotation, user-agent/header rotation, stealth browser mode, and integration points for CAPTCHA solving services. These can be configured per job.
Can I run it on a schedule?
Built-in cron-based scheduling lets you set recurring jobs. Combined with structured logging and notifications (email, webhook, Slack), you can monitor pipelines without manual intervention.
What export formats are supported?
CSV, Excel (.xlsx), JSON, Parquet, SQLite, Python Pickle, and HTML. The MultiExporter lets you output to several formats in a single pipeline run.
Is there a web interface?
Yes. A Flask-based web UI is included for managing jobs, editing configurations, and browsing results. The CLI is also available for scripting and automation.
What Python versions are supported?
Python 3.8 through 3.12. The package uses a modular architecture with no binary dependencies beyond standard data-science libraries.
Ready to standardize your data extraction?
Get a walkthrough of Data Scraper tailored to your team's workflows.