Data Scraping, Harvesting & Automation Pipelines

Web Scraping & Data Harvesting

If the data exists somewhere on the internet, I can get it. I build custom scrapers and harvesters that pull structured data from:

• Websites and web applications, including JavaScript-rendered pages
• Public and authenticated APIs
• PDFs, spreadsheets, and other document formats
• Directories, listings, and public databases

Every scraper is built to handle pagination, rate limits, retries, and edge cases. No fragile scripts that break after one run.

Common Data Sources

Every data source has its own quirks. I have worked with all of the major types and know how to handle each one reliably.

APIs and Web Services

Most modern platforms expose their data through REST or GraphQL APIs. I build integrations that authenticate, paginate, handle rate limits, and retry failed requests automatically. Whether it is a well-documented public API or a poorly documented internal one, I get the data flowing.

Databases

I connect directly to MySQL, PostgreSQL, MongoDB, SQLite, and other database systems. For cross-database workflows, I build extraction queries that pull exactly what you need without overloading your production systems.

Web Pages

Not every data source has an API. When the data lives on a website, I build scrapers that navigate pages, handle dynamic content rendered by JavaScript, and extract structured data from the HTML. I handle login walls, CAPTCHAs, and anti-bot protections where legally permitted.

Flat Files and Spreadsheets

CSV exports, Excel files, TSV dumps, and fixed-width text files are still everywhere in business. I build parsers that read these formats, handle encoding issues, and normalize the data into something your systems can actually use.

PDFs and Documents

PDF extraction is notoriously messy. I use a combination of text extraction, table detection, and OCR (optical character recognition) to pull structured data from PDF reports, invoices, and forms. The same applies to Word documents and other office formats.

Data Transformation

Raw data is rarely useful as-is. I build transformation layers that take messy, inconsistent source data and turn it into something clean and usable:

• Cleaning: strip whitespace, fix encoding, remove duplicates
• Normalizing: consistent formats for dates, phone numbers, addresses, currencies
• Restructuring: flatten nested data, merge sources, reshape for your target schema
• Enrichment: cross-reference with other datasets, geocode addresses, categorize records

Data Quality and Validation

Bad data costs more than no data. Every pipeline I build includes quality checks that catch problems before they reach your systems.

Deduplication

Duplicate records creep in from multiple sources, repeated imports, and inconsistent formatting. I build deduplication logic that identifies and merges duplicates using exact matching, fuzzy matching, and business rules specific to your data.

Error Detection

I validate data at every stage of the pipeline. Type checking, range validation, format verification, and referential integrity checks all run automatically. When something looks wrong, the system flags it for review instead of pushing bad data downstream.

Data Cleaning

Names with extra spaces. Phone numbers in twelve different formats. Addresses that are missing zip codes. I build cleaning rules that standardize your data so every record is consistent and usable. This is not a one-time fix. It runs on every batch, every time.

Monitoring and Alerting

Pipelines break. Sources change their format. APIs go down. I build monitoring into every pipeline so you know immediately when something fails or when data quality drops below your threshold. You get alerts, not surprises.

Automated Pipelines

One-off data pulls are fine, but the real value is in automation. I build pipelines that run on their own:

• Scheduled ETL jobs: hourly, daily, weekly, whatever the cadence
• Cron-based systems that run on your server or in the cloud
• Event-driven pipelines triggered by webhooks, file uploads, or database changes
• Monitoring and alerting so you know immediately when something fails or data looks wrong

Once it is running, it runs. You get fresh data without thinking about it.

Delivery

Data needs to land somewhere useful. I deliver to wherever your workflow lives:

• Databases: MySQL, PostgreSQL, MongoDB, SQLite
• APIs: push data to your existing systems via REST or webhook
• Spreadsheets: Google Sheets, Excel files, CSV exports
• Cloud storage: S3, Google Cloud Storage, Dropbox
• Email reports: formatted summaries delivered to your inbox

Your data, your destination, your schedule.

Real-World Use Cases

Data automation is not abstract. Here are some of the most common projects I build for clients.

Lead Generation

I build scrapers that collect business contact information from directories, social platforms, and industry listings. The data gets cleaned, deduplicated, and delivered to your CRM or outreach tool in a format that is ready to use. Fresh leads on a schedule, without manual research.

Price Monitoring

I track competitor pricing across e-commerce sites, marketplaces, and vendor catalogs. The pipeline runs daily or hourly, captures price changes, and delivers alerts or reports so you can adjust your pricing strategy in real time.

Competitive Intelligence

I monitor competitor websites, job postings, press releases, and social media activity. The data feeds into dashboards or reports that show you what your competitors are doing before they announce it publicly.

Reporting Dashboards

I pull data from multiple sources, including your CRM, analytics tools, advertising platforms, and financial systems. Then I transform it into a unified dataset that powers a custom dashboard. One place to see everything that matters to your business, updated automatically.

Content Aggregation

I build pipelines that collect content from RSS feeds, news sites, and industry publications. The data gets filtered, categorized, and delivered to your team or published to your own platform. Great for newsletters, research teams, and content curators.