Draft content

Batch Sales Analytics Pipeline

Draft case study for a batch ETL pipeline that turns raw sales exports into validated revenue metrics and dimensional tables.

PythonSQLPostgreSQLDocker

Problem

Raw transaction exports are difficult to use directly for reporting because field names, data types, duplicate records, and missing reference data can change between files.

Data Sources

Transaction CSV exports
Product reference data
Customer reference data
Daily file drops

Architecture

Raw files -> ingestion -> staging tables -> validation -> dimensional transformations -> revenue mart.

Pipeline Design

The pipeline loads raw files into staging tables, validates required columns, converts types, removes duplicate orders, and joins customer and product reference data. The transformed layer produces a fact table for orders and dimensions for customers and products.

Data Model

stg_transactions
dim_customer
dim_product
fact_orders
mart_daily_revenue

Data Quality Checks

Required fields are not null
Order IDs are unique
Product and customer IDs match reference tables
Daily row counts are recorded

Challenges

The main challenge is keeping the pipeline stable when raw files contain missing values, duplicate records, or unexpected product codes.

Outcome

This project produces a documented batch pipeline and an analytics-ready revenue mart. Replace this draft with real repository links, dataset details, run logs, and measured outputs.