Selected work

Project 4 of 4

Automation · Data pipeline engineering · Oct 2024 - Jan 2025

Data Pipelines

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

Role
Independent data-pipeline engineering
Deliverable
Validated CSV and JSON datasets
Focus
Extraction · cleaning · delivery
Repository

Overview

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

  • Python
  • Scrapy
  • BeautifulSoup
  • Pandas

Project flow

Architecture and flow.

5 connected stages

  1. 1sources
  2. 2extract
  3. 3clean
  4. 4validate
  5. 5handoff

Engagement basis

Freelance extraction and data-quality delivery

Independent client work · Remote engagements · 2024–2025

This case study is grounded in completed client delivery rather than an external research paper. Client datasets remain private.

Public method examples

Private client data excluded

Scope

Work completed and source material.

Scope and evidence

Scope and contribution.

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

  1. More than 10,000 records were processed and cleaned across the freelance work.

  2. Deliverables were prepared as structured CSV or JSON for downstream use.

  3. The public repository demonstrates scraping methods without client datasets.

Problem

Main technical constraint.

Problem framing

Handling inconsistent source data.

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.

Implementation

4 implementation steps.

Implementation

Implementation steps.

  1. Step1

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

  2. Step2

    Handled pagination while keeping collection code separate from cleaning code.

  3. Step3

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

  4. Step4

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

Result

Validated datasets for delivery.

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.