https://github.com/narius2030/sakila-datawarehouse-ssis

Implement a simple data warehouse to store Saklia data - Create data pipelines for extract, transform and load data from source to warehouse - Retrieve data in warehouse to explore and do several analysis
https://github.com/narius2030/sakila-datawarehouse-ssis

data-analysis data-integration data-modeling data-visualization excel microsoft-sql-server power-bi ssas ssis

Last synced: 6 days ago
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Implement a simple data warehouse to store Saklia data - Create data pipelines for extract, transform and load data from source to warehouse - Retrieve data in warehouse to explore and do several analysis

• Host: GitHub
• URL: https://github.com/narius2030/sakila-datawarehouse-ssis
• Owner: Narius2030
• Created: 2024-05-04T17:28:49.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-25T14:26:03.000Z (over 1 year ago)
• Last Synced: 2025-04-15T23:26:18.546Z (6 months ago)
• Topics: data-analysis, data-integration, data-modeling, data-visualization, excel, microsoft-sql-server, power-bi, ssas, ssis
• Language: TSQL
• Homepage:
• Size: 12.1 MB
• Stars: 3
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Introduction

### 1. Business Performance

`Revenue by employee`: Determine which employees bring the highest revenue to the store to evaluate their work performance by month/quarter/year

`Strengths of each employee`: Find out the types of customers suitable for each employee, from there you can assign or transfer the right type of customers according to their strengths

### 2. Rental Expense

`Strategic location`: observe customer purchasing and spending trends in countries or regions, thereby devising expansion strategies or providing products suitable for each region

`Golden hour`: observe times that bring in high revenue or sell in large quantities, at those hours or times. From there, promotional campaigns or attractive incentives will be deployed at those time frames, helping to attract many customers

### 3. Film Actor Analysis

`Favorite genres`: Track which genre movies are still unrented by quarter and year Which genre movies have the most inventory by quarter and year. Which genre's movies are in stock at least quarterly and annually? We can adjust our marketing and advertising strategies to increase the rental opportunities for these movies

`Cost of film rental`: Track which genre movies are rented with the highest price by quarter and year. Which genre movies are rented with the lowest price by quarter and year. We can identify the value of each type of movie in total revenue and adjust our pricing or calling strategy accordingly

## Data Pipeline Design

### 1. Time and Geography Pipeline

![image](https://github.com/Narius2030/Sakila-DataWarehouse-for-Business-Process/assets/94912102/bd7059fd-6c70-416e-a29e-2470be51e2b2)

### 2. Fact Table Pipeline

![image](https://github.com/Narius2030/Sakila-DataWarehouse-for-Business-Process/assets/94912102/af21e848-237c-4e7b-999d-ad06c649184b)

## Analysis Result

After build successfully a data warehouse, import data from it to hierarchal database by **SSAS** service. Then, load data to Power BI and Excel for analyzing

*A Example Dashboard*

![image](https://github.com/Narius2030/Sakila-DataWarehouse-for-Business-Process/assets/94912102/a22ba451-e5bc-454d-af9a-2c22c1706b5f)

## Data Warehouse Schema (Galaxy)

![image](https://github.com/Narius2030/Sakila-DataWarehouse/assets/94912102/a2114810-2d54-4d0a-a512-63099dd01266)