Scenario: Automating Sales Data Processing for a Retail Chain
Introduction:
A retail company operates multiple stores and an e-commerce platform,
generating large amounts of sales data daily. Currently, they rely on manual
reporting, leading to errors and delays. To solve this, an automated ETL
(Extract, Transform, Load) pipeline is designed to centralize sales data for
real-time analytics.
Step 1: Understanding Business Needs
The
company aims to:
- Data Integration: Combine sales data from
multiple sources.
- Data Cleaning &
Transformation:
Standardize formats and remove errors.
- Sales Analytics: Store processed data in a
data warehouse.
- Real-Time Reporting: Enable dashboards for decision-makers.
Step 2: ETL Pipeline Architecture
- Extract (E) – Collect raw data from:
- Store POS systems (SQL
databases)
- E-commerce platform (API,
JSON, CSV files)
- Third-party partners
(Google Sheets, Excel, REST APIs)
- Transform (T) – Clean and standardize data:
- Convert different formats
into a unified schema
- Handle missing values and
remove duplicates
- Apply business rules (e.g.,
currency conversion, product categorization)
- Load (L) – Store processed data in a
Data Warehouse (e.g., Amazon Redshift, Snowflake, Google BigQuery)
for reporting.
Step 3: ETL Pipeline Implementation in Python
1. Extract Data from Multiple Sources
import pandas as pd
# Extract from SQL database (store POS sales)
store_sales = pd.read_sql("SELECT * FROM store_sales", connection)
# Extract from CSV (e-commerce sales)
ecom_sales = pd.read_csv("ecommerce_sales.csv")
# Extract from API (third-party partners)
import requests
response =
requests.get("https://api.salesplatform.com/data")
partner_sales = pd.DataFrame(response.json())
2. Transform Data (Cleaning & Standardization)
# Standardizing column names
ecom_sales.rename(columns={"total_price":
"sales_amount"}, inplace=True)
partner_sales.rename(columns={"price": "sales_amount"}, inplace=True)
# Merging all sales data into one DataFrame
sales_data = pd.concat([store_sales, ecom_sales, partner_sales], ignore_index=True)
# Handling missing values
sales_data.fillna({'sales_amount': 0},
inplace=True)
# Converting date format
sales_data['sale_date'] = pd.to_datetime(sales_data['sale_date'])
3. Load Data into a Data Warehouse
from sqlalchemy import create_engine
# Connect to a PostgreSQL Data Warehouse
engine =
create_engine("postgresql://user:password@warehouse:5432/retail_db")
# Load transformed data into warehouse
sales_data.to_sql("sales_data", engine,
if_exists="replace", index=False)
Outcome: The ETL pipeline automates data
ingestion, cleaning, and storage for real-time analytics.
Step 4: Data Flow Diagram for ETL Pipeline
+----------------+
+----------------+
+----------------+
|
Store POS Data | | E-Commerce CSV
| | Partner API |
+----------------+
+----------------+
+----------------+
| | |
| | |
v v v
+--------------------------------------------------------------+
| Extract (E) |
+--------------------------------------------------------------+
|
v
+--------------------------------------------------------------+
| Transform (T) - Data
Cleaning |
| - Standardization - Handling Missing Data |
| - Data Merging - Business Rule Application |
+--------------------------------------------------------------+
|
v
+--------------------------------------------------------------+
| Load (L) - Data
Warehouse |
| (Amazon Redshift / Snowflake / Google
BigQuery) |
+--------------------------------------------------------------+
|
v
+--------------------------------------------------------------+
| Business Intelligence (Power BI,
Tableau, Looker) |
+--------------------------------------------------------------+
Insight: This pipeline ensures fast,
error-free, and centralized sales data processing for better
decision-making.
Step 5: Business Impact of the ETL Pipeline
Implementing
an automated ETL pipeline enables:
✅ 80% Faster Data Processing – Automates daily sales reporting. ✅ Reduced
Data Errors – Cleans and standardizes data automatically. ✅ Real-Time
Analytics – Provides live sales insights for decision-makers. ✅ Improved
Business Decisions – Optimized inventory, pricing, and marketing.
Interview Engagement (Closing Statement)
"This scenario demonstrates how a well-designed ETL pipeline
transforms raw retail sales data into business intelligence insights.
Would you like me to expand on scalability using Apache Airflow or Spark for
big data processing?"
No comments:
Post a Comment