How kowshikkodumuri/databricks_pyspark_project works
This repository presents a PySpark sales analytics pipeline: transactional and product data are ingested, cleaned, joined, transformed into profitability measures, and aggregated into business KPIs for persistence and reporting. The README documents the workflow and capabilities, but no implementation source was provided, so the graph captures documented relationships rather than verified function calls. The HTML file is named as an analysis project, but its contents and connection to the pipeline are not established.
Main entry points: README.md Data ingestion · customer_sales_enterprise_.json Customer sales · product_master_enterprise.json Product master
Components 10
Data inputs
- Customer sales
customer_sales_enterprise_.json - Product master
product_master_enterprise.json
Analytics workflow
- Data ingestion
README.md - Data cleaning
README.md - Join and derive
README.md - KPI analysis
README.md
Results
- Result persistence
README.md - Parquet / Delta
- Sales analysis HTML
sales_analysis_project.html
Other components
- Business stakeholders
Connections 8
- Customer sales → Data ingestion: loads JSON
- Product master → Data ingestion: loads JSON
- Data ingestion → Data cleaning: passes data
- Data cleaning → Join and derive: cleans data
- Join and derive → KPI analysis: provides measures
- KPI analysis → Result persistence: saves results
- Result persistence → Parquet / Delta: writes Parquet/Delta
- Business stakeholders → KPI analysis: uses insights