Production Data ETL & Reporting Pipeline

📅 January 20, 2023

Built a daily Python ETL pipeline that queried a web API for raw production data, performed complex conditional counting and summarization, and stored the data for downstream visualization in Power BI.

Skills: Pandas Matplotlib SQL Feature Engineering Python Requests ETL Data Aggregation Power BI DAX Power Query CSV Paraquet

Overview

This project focused on automating the ingestion, transformation, and delivery of daily production data to support reliable reporting and operational decision-making. Prior to this work, key production metrics were generated manually, resulting in delays, inconsistent results, and limited visibility for downstream users.

The goal of the project was to create a repeatable, automated pipeline that produced a trusted, analysis-ready dataset each day and fed directly into reporting tools used by engineering, quality, and manufacturing teams.

Problem

Production data was available through a web API, but accessing and preparing it for reporting required manual effort each day. This created several issues:

  • Daily metrics required manual data pulls and spreadsheet-based processing
  • Report creation was time-consuming and prone to inconsistency
  • Stakeholders lacked timely access to up-to-date production performance
  • Analysts spent more time preparing data than analyzing it

As reporting demand increased, the existing approach did not scale.

Solution

I built an automated ETL pipeline in Python to reliably collect, process, and store daily production data for downstream reporting.

Key components of the solution included:

  • Querying a production data API using Python
  • Parsing, cleaning, and validating raw records
  • Performing conditional counting and aggregation logic to compute key metrics
  • Storing transformed datasets in analysis-ready formats for Power BI consumption
  • Scheduling the pipeline to run daily and refresh reporting automatically

The pipeline was designed to be robust, readable, and easy to maintain as reporting needs evolved.

Impact

  • Automated ingestion and preparation of 50k+ production records per day
  • Reduced daily metric report creation time from ~1 hour to ~5 minutes
  • Enabled multiple teams to access up-to-date production metrics each morning
  • Improved consistency and trust in reported metrics by standardizing transformations
  • Provided the data foundation for the Process Data Dashboard Creation project

Coming Soon!

Code Repository

Due to proprietary production systems and data, source code is not publicly available.