Today we have 24 hours to create a full end-to-end data warehouse using state-of-the-art technology from the world of 

data engineering like DuckDB and dbt, okay?

# Start with Choosing the Data Set to Model

Here, I'll introduce you to a dataset from Kaggle called the "Instabul Data Set." 

Welcome to the shopping world of Istanbul! Our dataset contains shopping information from 10 different shopping malls between 2021 and 2023. We have gathered data from various age groups and genders to provide a comprehensive view of shopping habits in Istanbul. The dataset includes essential information such as invoice numbers, customer IDs, age, gender, payment methods, product categories, quantity, price, order dates, and shopping mall locations. We hope that this dataset will serve as a valuable resource for researchers, data analysts, and machine learning enthusiasts who want to gain insights into shopping trends and patterns in Istanbul. Explore the dataset and discover the fascinating world of Istanbul shopping!

## **Content**

Attribute Information:

- **invoice_no:** Invoice number. Nominal. A combination of the letter 'I' and a 6-digit integer uniquely assigned to each operation.
- **customer_id:** Customer number. Nominal. A combination of the letter 'C' and a 6-digit integer uniquely assigned to each operation.
- **gender:** String variable of the customer's gender.
- **age:** Positive Integer variable of the customers age.
- **category:** String variable of the category of the purchased product.
- **quantity:** The quantities of each product (item) per transaction. Numeric.
- **price:** Unit price. Numeric. Product price per unit in Turkish Liras (TL).
- **payment_method:** String variable of the payment method (cash, credit card or debit card) used for the transaction.
- **invoice_date:** Invoice date. The day when a transaction was generated.
- **shopping_mall:** String variable of the name of the shopping mall where the transaction was made.

The data in the image appears to be a table of customer purchase data. Here is a formatted version of the table:

| **Invoice No.** | **Customer ID** | **Gender** | **Age** | **Category** | **Quantity** | **Price** | **Payment Method** | **Invoice Date** | **Shopping Mall** |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 113884 | C241288 | Female | 28 | Clothing | 5 | 1500.40 | Credit Card | 5/8/2022 | Canyon |
| I17333 | C111565 | Male | 21 | Shoes | 3 | 1800.51 | Debit Card | 12/12/2021 | Forum Istanbul |
| I172701 | C266599 | Male | 20 | Clothing | 1 | 300.08 | Cash | 9/11/2021 | Metrocity |
| I173702 | C988172 | Female | 66 | Shoes | 5 | 3800.95 | Credit Card | 16/09/2021 | Metropol AVM |
| I37046 | C189076 | Female | 53 | Books | 4 | 60.00 | Cash | 24/10/2021 | Canyon |

Q- First question : is this Data Set optimized for this particular project ok

Q- i mean it’s optimized for a “OLAP” data modeling like start schema yes or not 

Q- i just want to give me the answer as “yes” or “non” oki 

Q- i mean did we can convert it into a “star” schema yes or non ? 

—> the answer is yes 

- so now we have confirmed this data Set choice let’s try to understand it more critically ok

Q- so the next question we  i will ask you some question about each feature (col) , and please try to give your answer okii 

Q- give me a brief description about the first col oki 

The first column, **Invoice No.**:  This is the unique identifier for each transaction. It's a nominal value, meaning it's used for identification and categorization, not for mathematical calculations. The format appears to be the letter "I" followed by a six-digit integer (though one example, 113884, lacks the "I").  This discrepancy will require cleaning during data preparation.  This column will be a key component of the fact table in our star schema, linking to dimension tables.

Q- the next one is called the “Customer ID” 

**Customer ID:** This column uniquely identifies each customer. Like Invoice No., it's a nominal value. The format is "C" followed by a six-digit integer. This will be a key component linking the fact table to a customer dimension table, allowing us to analyze sales by customer demographics and other customer-related attributes.

Q- the next one is called “Gender” 

**Gender:** This column represents the customer's gender, likely categorized as "Male" or "Female."  It's a categorical variable and will be an attribute in the customer dimension table. This allows for analysis of purchasing behavior based on gender.  It's important to consider how to handle any missing or non-binary gender identifications during data cleaning and transformation.

Q- the next one is called “Age” 

**Age:** This column indicates the customer's age, represented as a positive integer. It will be an attribute within the customer dimension table.  Age allows for analysis based on customer age groups (e.g., segmenting customers into age brackets for targeted marketing).  It's worth noting that age is a "slowly changing dimension," meaning it changes over time. This needs to be considered when designing the dimension table (e.g., using a slowly changing dimension type 2 to track historical age changes if needed).

Q- the next one is called “Category”

**Category:** This column describes the category of the purchased product (e.g., "Clothing," "Shoes," "Books"). It's a categorical variable and will be a key component of a product category dimension table.  This allows for analysis of sales by product category and potentially hierarchies of categories (if they exist, e.g., "Clothing" -> "Shirts" -> "T-Shirts"). This column will help answer questions like which product categories are most popular.

Q- the next one is called “ Quantity” 

**Quantity:** This column specifies the number of units of a particular product purchased in a given transaction.  It's a numerical value and will be a crucial part of the fact table. It's essential for calculating total sales amounts and analyzing purchasing quantities by product, customer, etc.

Q- the next one is called “Price” 

**Price:** This column represents the unit price of the product in Turkish Liras (TL).  It's a numerical value and, along with Quantity, will be used to calculate the total amount for each line item in the fact table.  This allows for analysis of revenue, average purchase value, and other price-related metrics.

Q- the next one is called “Payment method” 

**Payment Method:** This column indicates how the transaction was paid (e.g., "Cash," "Credit Card," "Debit Card"). It's a categorical variable and will form a payment method dimension table. This enables analysis of sales by payment method, identifying trends and preferences in how customers pay.

Q- the next one is called “Invoice Date” 

**Invoice Date:** This column records the date of the transaction. It will be a key component of a date dimension table.  This dimension table should be designed to enable time-based analysis, allowing us to break down sales by day, week, month, year, etc.  This is crucial for trend analysis and understanding seasonality.  The date should be stored in a proper date format for efficient querying.

Q- the next one is called “Shopping Mall” 

**Shopping Mall:** This column specifies the location where the purchase occurred. It's a categorical variable and will form a shopping mall dimension table. This allows for geographic analysis of sales, identifying which malls perform best, comparing sales across different locations, and potentially even incorporating geographical data (e.g., mapping sales density).

# Data Modeling

Now that we have defined the objective and obtained the data, it's time to start iterating on the project at hand. We'll begin with the modeling part of the data warehouse. 

Since we've chosen to use a star schema for this project, let's quickly review the principles behind this data architecture choice.

## Star Schema: A "Feynman" Technique

i am huge beliver that “Feynman” is one the langendry educator ever lived , this technique is inspired a lot of educator , and how he explain complex topics using a unqiue style of execution made me inspired a lot , this is why i think using the context we have so far about the project i want you to help me build a mental model and explain it the concept of “Star Schema” using his famous technique ok