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Fetching and Analyzing Warehouse Inventory Data using Python | GCP | Set 02

3 Scenarios
1 Hour 25 Minutes
Intermediate
project poster
Industry
retail-and-cpg
Skills
approach
quality
data-understanding
data-storage
data-wrangling
programming
data-quality
data-visualization
Tools
python
google-cloud

Learning Objectives

Fetch data from an API and handle responses efficiently
Process and store structured data in a suitable format
Retrieve and validate data from a data lake storage system
Perform analytical operations on the combined dataset
Create data visualizations to derive meaningful insights

Overview

GlobalMart, a global e-commerce retailer, operates multiple warehouses to store and dispatch inventory efficiently. The Operations Team faces several challenges in managing warehouse inventory data effectively:

  • Semi-structured Data Format: The warehouse inventory data is available through an API endpoint, returning nested JSON responses that are difficult to process directly.

  • Need for Automation: Currently, analysts manually extract key insights from the warehouse inventory reports, leading to errors and inefficiencies.

  • Scalability Issues: Given the vast volume of data spanning thousands of products across multiple warehouses, processing it efficiently becomes complex. A structured, object-oriented approach is essential to manage and handle this data programmatically.

  • Reusable and Maintainable Code: The current scripts are monolithic and unstructured, making it difficult to modify and maintain.

Your Task:

As a Data Engineer, your job is to fetch and analyze warehouse inventory data from GlobalMart’s API.

Prerequisites

  • Basic proficiency in Python (functions, loops, and error handling)
  • Understanding of API requests using the requests library
  • Experience working with JSON data (parsing, flattening, and processing)
  • Familiarity with data lakes and working with files in cloud storage (GCS)
  • Knowledge of Pandas, Numpy, Matplotlib, etc., for data manipulation and transformation