https://github.com/shayartt/retails_analysis

Scala Spark project
https://github.com/shayartt/retails_analysis

Last synced: about 2 months ago
JSON representation

Scala Spark project

• Host: GitHub
• URL: https://github.com/shayartt/retails_analysis
• Owner: Shayartt
• Created: 2024-07-23T18:16:02.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-26T01:56:12.000Z (7 months ago)
• Last Synced: 2024-11-05T21:08:45.367Z (3 months ago)
• Language: HTML
• Size: 59.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Retail Sales Analytics Platform

## Overview

This project aims to build a comprehensive retail sales analytics platform using the following tools:

- **Spark in Databricks (Scala)**: For ETL (Extract, Transform, Load) processes.

- **DBT (Data Build Tool)**: For data transformation and modeling.

- **Great Expectations**: For data quality and validation.

- **Apache Superset**: For data visualization and reporting.

### Diagram Design

(Will be updated into a better version soon)

![Diagram](https://github.com/user-attachments/assets/3c7a5986-3316-415a-989c-39008004e45b)

## Project Steps

1. **Data Ingestion**

   - Collect sample retail sales data (e.g., transactions, product information, customer data) from open datasets or generate synthetic data.

   - Use Spark to ingest this data into a Databricks environment.

2. **Data Processing with Spark & Databricks**

   - Clean and preprocess the data using Spark within Databricks.

   - Perform necessary transformations (e.g., calculating total sales, aggregating data by product category).

   - Rename schema, database, and table names to best practices (e.g., `prod.bronze_retails.sales`).

3. **Data Modeling with DBT**

   - Set up a DBT project and connect it to your Databricks environment.

   - Create DBT models to transform the preprocessed data into a star schema or other appropriate data models.

   - Write SQL-based transformations in DBT and run them to create the final data models.

4. **Data Quality with Great Expectations**

   - Integrate Great Expectations into your DBT pipeline.

   - Define and run data quality checks to ensure the integrity and accuracy of your transformed data.

   - Configure alerts for data quality issues.

5. **Data Visualization with Apache Superset**

   - Set up Apache Superset and connect it to your Databricks/DBT output database.

   - Create interactive dashboards and reports to visualize key metrics such as sales trends, customer demographics, and inventory levels.

   - Use various chart types and visualizations to make data insights clear and actionable.

6. **Final Integration and Automation**

   - Automate the data pipeline using Databricks jobs or another orchestrator.

   - Schedule regular runs of your DBT models and Great Expectations checks.

   - Update your Apache Superset dashboards to reflect the latest data.

## Deliverables

- Databricks notebook with Spark data processing code.

- DBT project with SQL-based transformation models.

- Great Expectations configurations and results.

- Apache Superset dashboards and reports.

## Learning Outcomes

- Gain hands-on experience with DBT for data transformation and modeling.

- Learn how to ensure data quality using Great Expectations.

- Develop skills in creating interactive and insightful visualizations with Apache Superset.

- Strengthen your existing knowledge of Spark and Databricks in a comprehensive data pipeline.