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Automation / Data Pipeline Engineering / Oct 2024 - Jan 2025

Data Pipelines

Python scraping pipelines that cleaned and delivered more than 10,000 records.

I built Python pipelines that collected public web data, cleaned it, checked it, and delivered structured CSV or JSON files for client use.

Explore the repository

How it works

  1. 01sources
  2. 02extract
  3. 03clean
  4. 04validate
  5. 05handoff

What is included

This page explains the public process: extraction, pagination, cleaning, validation, and delivery. The linked repository contains examples without client data.

  • E1More than 10,000 records were processed and cleaned across the freelance work.
  • E2Deliverables were prepared as structured CSV or JSON for downstream use.
  • E3The public repository demonstrates scraping methods without client datasets.

The challenge

What the project needed to do.

The source sites had pagination, inconsistent fields, duplicate records, formatting noise, and site specific edge cases. The pipelines needed to handle those issues and produce consistent output.

How I built it

Break the work into small, testable steps.

  1. 01

    Used Scrapy and BeautifulSoup to write extraction code for each source.

  2. 02

    Handled pagination while keeping collection code separate from cleaning code.

  3. 03

    Used Pandas checks to normalize fields, review output quality, and prepare CSV or JSON files.

  4. 04

    Documented how to collect, clean, and export the data.

Result

What I finished.

The pipelines cleaned more than 10,000 records and delivered CSV or JSON files for analysis and machine learning use. The public repository does not include client data.

What I learned

Scraping was only the first step. Most of the work was making thousands of records consistent, checkable, and useful.