Turn Raw Web Pages into Structured, Analytics-Ready Data
Our data scraping teams design and operate fully-managed pipelines using Python, Scrapy, BeautifulSoup, Playwright, Selenium, and REST APIs to continuously extract data from websites, portals, and online services, transforming it into clean tables, files, and data streams that integrate seamlessly with your transforming it into clean tables, files, and data streams that integrate seamlessly with your storage and reporting systems.
Data Types
Prices, products, reviews, jobs, news, listings, profiles, documents.
Deliverables
CSV, JSON, Parquet, relational DBs, CSV, JSON, Parquet, relational databases, file systems..
Freshness
Real-time, hourly, daily, or custom schedules with alerts.
Compliance
Robust robots.txt checks, rate limiting, and legal-risk aware design.
What is Data Scraping?
Data scraping is the automated extraction of information from websites, portals, and online systems using Python-based scripts, web crawlers like Scrapy, headless browsers such as Playwright and Selenium, and RESTful APIs to collect large volumes of structured data at scale.
From price monitoring and competitor tracking to lead collection and content aggregation, data scraping powered by proxy networks, scheduling systems, cloud storage, and scalable scraping pipelines enables businesses to access reliable, up-to-date web data.
Data Scraping Architecture at Oodles AI
Source Discovery & Mapping
Identify and map all relevant web sources—sites, portals, search pages, and APIs—using custom crawlers, XPath/CSS selectors, and API schemas, while defining pagination, filters, and refresh frequency for each source.
Resilient Extraction Layer
Hybrid crawlers built with Scrapy, Requests, Playwright, Selenium, and managed proxy rotation services to handle JavaScript rendering, session management, CAPTCHAs, rate limits, and anti-bot protections.
Normalization, Deduplication & QA
Clean and standardize fields using Python, Pandas, and validation frameworks, remove duplicates, detect schema changes, validate completeness, and flag anomalies before loading data into downstream systems.
End-to-End Data Scraping Workflow
We follow a structured, transparent delivery model so your team understands exactly how web data moves from source to delivery.
1. Discovery & Scoping
Define business goals, target sites, fields, refresh frequency, formats, and compliance constraints.
2. Prototype Scraper & Schema
Build a pilot scraper for a subset of pages, design the output schema, and validate data quality with your team.
3. Scale-Up & Hardening
Extend coverage to all target sources, add proxy rotation, throttling, error handling, and monitoring.
4. Integration & Ongoing Operations
Connect scrapers to your storage and analytics stack, then manage break-fix, schema changes, and new data needs over time.
Where Data Scraping Helps the Most
- Continuous price, catalog, and promotion monitoring
- Lead enrichment and firmographic data collection
- Content, SEO, and competitive intelligence
- Alternative data feeds for risk and investment models
Ready to build Data Scraping solutions? Let's talk
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