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

Absolutely! Let's dive into the **Star Schema** using the **Feynman Technique**, inspired by the legendary educator Richard Feynman. This approach will help us build a clear and intuitive mental model for your data warehouse project using the Istanbul Data Set.

---

### **1. Choose a Concept: Star Schema**

**Star Schema** is a way to organize data in a data warehouse to make it easy to retrieve and analyze. Imagine it as a star, with a central point connected to various points around it.

---

### **2. Teach It to a Child**

**Imagine You're Building a Toy Store's Sales Board**

Let's say you have a big board to track all the sales in your toy store. You want to know things like:

- **Who** is buying toys?
- **What** toys are being bought?
- **When** are they buying them?
- **Where** in the store they're buying them.
- **How** they're paying for the toys.

To make this board simple and organized, you decide to use the **Star Schema** method.

### **Central Part: The Fact Table**

At the center of your board is a big circle called the **Fact Table**. Think of it as the main record of all sales. Each sale has details like:

- **Invoice No.**: The unique number for each sale.
- **Customer ID**: Who bought the toy.
- **Quantity**: How many toys were bought.
- **Price**: How much each toy costs.
- **Total Amount**: Quantity × Price.

### **Spokes: Dimension Tables**

Radiating out from the Fact Table are smaller circles called **Dimension Tables**. These are like different sections of your board that give more details about each aspect of the sale.

1. **Customer Dimension**
    - **Customer ID**: Matches the one in the Fact Table.
    - **Gender**: Whether the customer is male or female.
    - **Age**: How old the customer is.
2. **Product Dimension**
    - **Product ID**: A unique ID for each toy category.
    - **Category**: What type of toy it is (e.g., Clothing, Shoes).
3. **Date Dimension**
    - **Date**: The day the sale happened.
    - **Month**: The month of the sale.
    - **Year**: The year the sale happened.
4. **Payment Dimension**
    - **Payment Method**: How the customer paid (e.g., Cash, Credit Card).
5. **Store Dimension**
    - **Shopping Mall**: Which mall the store is in.

---

### **3. Identify Gaps and Fill Them**

**Question:** Why do we connect these Dimension Tables to the Fact Table?

**Answer:** By connecting these Dimension Tables to the Fact Table, we can easily ask questions like:

- **Who** buys the most toys? (Using Customer Dimension)
- **What** category of toys sells best? (Using Product Dimension)
- **When** do we have the highest sales? (Using Date Dimension)
- **How** do customers prefer to pay? (Using Payment Dimension)
- **Where** are our best stores? (Using Store Dimension)

This structure makes it simple to filter and analyze sales data from different angles without confusion.

---

### **4. Review and Simplify**

**Simplified Summary:**

- **Star Schema** is like a sun with a central Fact Table (all sales data) and surrounding Dimension Tables (details about customers, products, dates, payments, and stores).
- This setup makes it easy to answer important business questions by connecting different pieces of information.
- It's organized and efficient, just like having labeled sections on your sales board to find answers quickly.

---

### **Applying It to Your Istanbul Data Set**

Let's map the Istanbul Data Set to our Star Schema:

- **Fact Table: Sales**
    - **invoice_no**
    - **customer_id**
    - **quantity**
    - **price**
    - **total_amount** (calculated as quantity × price)
    - **payment_method**
    - **invoice_date**
    - **shopping_mall**
- **Dimension Tables:**
    1. **Customer Dimension**
        - **customer_id**
        - **gender**
        - **age**
    2. **Product Dimension**
        - **category** (you might want to add a unique **category_id**)
    3. **Date Dimension**
        - **invoice_date**
        - **month**
        - **year**
    4. **Payment Dimension**
        - **payment_method**
    5. **Store Dimension**
        - **shopping_mall**

**Visual Representation:**

            `Customer Dimension
                  |
                  |
                  |
Payment Dimension----Sales (Fact Table)----Product Dimension
                  |
                  |
                  |
              Store Dimension
                  |
                  |
                  |
            Date Dimension`

---

### **Benefits of Using Star Schema**

1. **Simplicity:** Easy to understand and navigate.
2. **Performance:** Optimized for quick data retrieval and analysis.
3. **Flexibility:** Easily accommodates new types of queries and data.
4. **Scalability:** Handles large volumes of data efficiently.

---

### **Conclusion**

Using the **Star Schema** for your Istanbul Data Set will allow you to efficiently analyze shopping trends, understand customer behaviors, and make informed business decisions. By organizing your data into a central Fact Table connected to relevant Dimension Tables, you create a powerful and intuitive data warehouse that's both easy to use and highly effective.

Let's move forward with building this star-shaped data warehouse using tools like **DuckDB** for fast query processing and **dbt** for managing the transformation of your data. Together, they'll help you create a robust and insightful data environment within your 24-hour project goal!

Q- oky this the first iteration 

Q- i am not sure about if it’s true yes or non 

Q- but can you help me by your fedback right not if my first iteration of modeling 

Q- is a good one yes or non oki 

Q- and build good argument like a  discussion style oky 

Okay, let's discuss your first iteration of the data model. Overall, it's a solid start and definitely on the right track for a star schema, but there are a few points we should discuss to refine it.

**Good Points:**

- **Clear Star Schema Structure:** You've correctly identified the fact table (Sales) and the surrounding dimensions (Customer, Product, Date, Payment, Store). The relationships between them are also well-defined.
- **Appropriate Fact Table:** The Sales fact table contains the core transactional information (invoice_no, customer_id, quantity, price, and the calculated total_amount). Including payment_method, invoice_date, and shopping_mall in the fact table is also correct, as these are foreign keys linking to your dimensions.
- **Well-Chosen Dimensions:** The dimensions you've selected capture the key aspects of the business process and provide the necessary context for analyzing sales data.

**Points for Discussion and Improvement:**

- **Product Dimension:** You've mentioned adding a category_id. This is *highly recommended*. Relying solely on the category name for joining can lead to performance issues and data integrity problems if there are typos or inconsistencies in the category names. A unique integer ID for each category is best practice. Consider adding more product-related attributes if available (e.g., brand, product name, etc.) to enrich your analysis possibilities.
- **Date Dimension:** While including invoice_date, month, and year is a good start, a date dimension can be much more powerful. Consider adding attributes like day of the week, week number, quarter, holiday flags, etc. These pre-calculated attributes will greatly simplify time-based analysis and reporting. There are many readily available scripts and dbt packages to generate a robust date dimension.
- **Payment Dimension:** This is currently a single attribute dimension. Consider if there's any additional information you might want to track related to payment methods. For example, for credit card payments, you might want to capture the card type (Visa, Mastercard, etc.) if that information is available. If not, the current model is probably sufficient.
- **Store Dimension:** Similar to the Product dimension, consider adding more descriptive attributes about the shopping malls. For example, you could add location (city, district), size (square footage), or even a flag indicating if the mall is a premium or outlet mall. These can add valuable context for geographic analysis.
- **Fact Table Granularity:** You've chosen a granularity of one row per invoice line item, which is typically a good choice for retail data. However, depending on your reporting needs, you might want to consider if a different granularity is appropriate (e.g., one row per invoice).
- **Data Cleaning and Transformation:** Your earlier comments about the "I" prefix inconsistency in invoice_no are crucial. Make sure to address this during data cleaning in your dbt models. Also, validate data types and handle missing values appropriately.
- **Surrogate Keys:** While not strictly required, consider adding surrogate keys (integer primary keys) to all your dimension tables. This can improve query performance and simplify joins, especially in larger datasets.

**In Summary:**

Your first iteration is a good foundation. By addressing the points above, particularly enriching the dimensions and ensuring data quality through dbt, you will create a more robust and analytical star schema optimized for your 24-hour project. Remember, iterative development is key – build a good foundation and enhance it as you go.

Q- do you share the same opionion here yes or non , 

Yes, I share the same opinion. Your first iteration of the data model is a solid foundation for implementing a star schema. Here’s a summary of why it's a good start:

### **Strengths:**

1. **Clear Identification of Fact and Dimension Tables:**
    - **Fact Table (Sales):** You've correctly identified the central fact table that captures the transactional data.
    - **Dimension Tables:** The surrounding dimensions (Customer, Product, Date, Payment, Store) effectively cover the key aspects needed for comprehensive analysis.
2. **Appropriate Attributes:**
    - Each dimension table includes relevant attributes that will enable detailed and meaningful analysis, such as gender and age in the Customer Dimension or category in the Product Dimension.
3. **Logical Relationships:**
    - The relationships between the fact table and dimension tables are well-defined, facilitating easy joins and efficient querying.

### **Areas for Improvement:**

1. **Enriching Dimension Tables:**
    - **Product Dimension:** Adding a unique `category_id` and possibly more attributes like `brand` or `product_name` can enhance data integrity and analytical depth.
    - **Date Dimension:** Expanding with attributes like `day_of_week`, `quarter`, or `holiday_flag` can provide richer time-based analysis.
2. **Data Quality and Cleaning:**
    - Addressing inconsistencies, such as the missing "I" prefix in `invoice_no`, is crucial for maintaining data integrity.
    - Implementing surrogate keys in dimension tables can improve performance and simplify joins.
3. **Granularity and Surrogate Keys:**
    - Ensuring the fact table's granularity aligns with reporting needs is important. Additionally, using surrogate keys can enhance query performance and scalability.
4. **Additional Attributes:**
    - Incorporating more descriptive attributes in dimension tables (e.g., location details in the Store Dimension) can provide deeper insights and more flexible reporting capabilities.

### **Conclusion:**

Your initial model effectively sets up the core structure of a star schema, making it suitable for OLAP-type queries and analysis. By addressing the areas for improvement, you'll enhance the model's robustness, scalability, and analytical power. This iterative refinement approach aligns well with best practices in data engineering and will help you build a comprehensive and efficient data warehouse within your project timeline.

Great job on laying the groundwork, and with these refinements, your star schema will be well-equipped to handle various analytical needs!

Q- now let’s try to generate the Atrifact of this Data Model oki 

Q- can you help by building the shema that respect this configuration 

https://claude.site/artifacts/73bd499c-fa35-450c-9a29-aa2186148aaa

this the code for the schema 

erDiagram
FACT_SALES {
int sale_id PK
string invoice_no
int customer_key FK
int product_key FK
int date_key FK
int payment_key FK
int store_key FK
int quantity
decimal price
decimal total_amount
}

```
DIM_CUSTOMER {
    int customer_key PK
    string customer_id
    string gender
    int age
}

DIM_PRODUCT {
    int product_key PK
    int category_id
    string category_name
}

DIM_DATE {
    int date_key PK
    date invoice_date
    int year
    int month
    int day
    string day_of_week
    int quarter
    boolean is_weekend
    boolean is_holiday
}

DIM_PAYMENT {
    int payment_key PK
    string payment_method
}

DIM_STORE {
    int store_key PK
    string shopping_mall
    string location
    string mall_type
}

FACT_SALES }|--|| DIM_CUSTOMER : has
FACT_SALES }|--|| DIM_PRODUCT : has
FACT_SALES }|--|| DIM_DATE : has
FACT_SALES }|--|| DIM_PAYMENT : has
FACT_SALES }|--|| DIM_STORE : has

```

Q- now it’s time to start coding here : 

Q- i think you have all the need it context here 

Q- just to khnow what i have done so far ok 

Q- i just download the data from kaggle and add it this local folder oky 

C:\Users\PCS\Desktop\Articals\Istunbull-Data\customer_shopping_data.csv\customer_shopping_data.csv

Q- i use a windows pc 

Q- now i want to start python env where i can isloate all the packgaes for this project okii 

i just use theses commands 

PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\customer_shopping_data.csv> python -m venv venv
PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\customer_shopping_data.csv> venv\Scripts\activate

Q- now i think it’s time to start coding : 

Q- so how to work with dbt core (open source ) and duckdb 

to configure the project before starting coding oky 

Q- give me step by step tutor to do this workflow oki