Sales ETL Pipeline & Business Performance Dashboard
Sales ETL Pipeline & Business Performance Dashboard
Embedded Files
Built an ETL Pipeline using Python to clean messy and inconsistent CSV Dataset then export to Google Sheet and use Looker Studio for Dashboard Analysis
Built an ETL Pipeline using Python to clean messy and inconsistent CSV Dataset then export to Google Sheet and use Looker Studio for Dashboard Analysis
📖 PROJECT INFORMATION
📖 PROJECT INFORMATION
Project Overview
Project Overview
This project was inspired by a job posting requiring the processing and cleaning of large CSV datasets ranging from 500MB to 1.7GB. Due to the limitations of Apps Script when handling large files, I designed and implemented a Python-based ETL pipeline to automate data cleaning, transformation, validation, reporting, and dashboard generation.
Problem
Problem
The challenge was to create a scalable workflow capable of:
Processing large datasets
Cleaning inconsistent values
Standardizing records
Generating business reports
Delivering interactive dashboards
⚙️ TECHNICAL DETAILS
⚙️ TECHNICAL DETAILS
Project Workflow
Project Workflow
Raw CSV
↓
Python ETL
↓
Data Cleaning
↓
Fuzzy Matching
↓
SQL Queries
↓
Google Sheets API
↓
Google Sheets Reporting
↓
Looker Studio Dashboard
Key Features
Key Features
ETL Pipeline
ETL Pipeline
Automated data extraction and transformation
Data validation and auditing
Structured project architecture
Data Quality
Data Quality
Fuzzy matching for value standardization
Missing value handling
Audit logging
Reporting
Reporting
KPI generation
Monthly sales reporting
Product analysis
Category analysis
Regional analysis
Dashboard
Dashboard
Interactive filters
KPI monitoring
Revenue trend analysis
Product, category, and region breakdowns
Tools Used
Tools Used
Python
Pandas
SQLite / SQL
Google Sheets API
Google Cloud Platform
Google Sheets
Looker Studio
🖼️IMPLEMENTATION GALLERY
🖼️IMPLEMENTATION GALLERY
FOLDER STRUCTURE
Purpose:
Modular project architecture designed to separate ETL processes, data storage, integration tools, and testing workflows.
ETL MODULE
Purpose:
Dedicated extraction, transformation, loading, and audit components for maintainability and scalability.
DATA & SQL LAYER
Purpose:
Organized storage for raw data, cleaned outputs, databases, and reporting queries.
INTEGRATION LAYER
Purpose:
Google Sheets API integration and fuzzy matching utilities for automated reporting.
📊 RESULTS & OUTCOMES
📊 RESULTS & OUTCOMES
GOOGLE SHEETS REPORTING LAYER
LOOKER STUDIO DASHBOARD
Business Impact / Results
Business Impact / Results
This project demonstrates how large and inconsistent CSV datasets can be transformed into reliable business reporting systems through automated ETL processes, SQL-based validation, Google Sheets integration, and interactive dashboarding.
Key Learnings
Key Learnings
ETL design principles
Large dataset processing
API authentication and integration
SQL reporting workflows
Dashboard development using Looker Studio
📥 DOWNLOADS
📥 DOWNLOADS
This project was created to deepen my understanding of ETL Pipeline, SQL, and App Integration using Python. By using python to clean, structure, auto correct, then connect to Google Sheet the cleaned CSV File, SQL Queries of the report, and finally use Looker Studio for data analysis.
This project was created to deepen my understanding of ETL Pipeline, SQL, and App Integration using Python. By using python to clean, structure, auto correct, then connect to Google Sheet the cleaned CSV File, SQL Queries of the report, and finally use Looker Studio for data analysis.
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