Q-  have done this work for now 

“ 
Certainly! Here is the formatted version of your guide:

Setting Up Your Data Warehouse Project Using dbt Core and DuckDB
Absolutely! Let's walk through setting up your data warehouse project using dbt Core and DuckDB in your Python virtual environment on your Windows PC. This step-by-step guide will cover:
Setting Up Your Python Environment
Installing DuckDB and dbt
Initializing a dbt Project
Configuring dbt to Use DuckDB
Loading Your Data into DuckDB
Creating dbt Models Based on Your Star Schema
Running and Testing Your dbt Project
Let's get started!
1. Setting Up Your Python Environment
You've already created and activated your virtual environment. Here's a quick recap and verification:
Steps:
Navigate to Your Project Directory:
PS C:\Users\PCS\Desktop\Articals\Istunbull-Data>
Activate Your Virtual Environment:
PS C:\Users\PCS\Desktop\Articals\Istunbull-Data> venv\Scripts\activate
Verify Activation: Your command prompt should now show (venv) indicating the virtual environment is active.
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data>
2. Installing DuckDB and dbt
a. Install DuckDB DuckDB can be used as an embedded analytics database. We'll install it as a Python package to interact with it directly.
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data> pip install duckdb
b. Install dbt Core and the DuckDB Adapter As of the latest updates, dbt supports DuckDB through the dbt-duckdb adapter. Installing dbt-duckdb will also install dbt-core.
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data> pip install dbt-duckdb
Note: If dbt-duckdb is not available or you encounter issues, ensure you're using the latest version of dbt and check the official dbt documentation for updates on DuckDB support.
c. Verify Installations Ensure both DuckDB and dbt are installed correctly.
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data> python -c "import duckdb; print(duckdb.__version__)"
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data> dbt --version
You should see the versions printed without any errors.
3. Initializing a dbt Project
a. Choose a Project Directory Decide where you want your dbt project to reside. We'll create a new folder within your current directory.
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data> mkdir istanbul_dbt_project
b. Initialize the dbt Project Navigate into the project directory and initialize a new dbt project.
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data> cd istanbul_dbt_project
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project> dbt init istanbul_dbt
You'll be prompted with some questions during initialization. Follow the prompts:
Profile Name: You can name it istanbul_dbt or any name you prefer.
Adapter Selection: Choose duckdb from the list.
This command will create the following structure:
istanbul_dbt_project/
├── istanbul_dbt/
│   ├── models/
│   │   ├── example/
│   │   │   └── ... (example models)
│   ├── dbt_project.yml
├── logs/
├── target/
└── ...
4. Configuring dbt to Use DuckDB
a. Locate and Edit profiles.yml dbt stores connection details in a profiles.yml file. On Windows, it's typically located at C:\Users\<YourUsername>\.dbt\profiles.yml.
Open profiles.yml: Navigate to C:\Users\<YourUsername>\.dbt\ and open profiles.yml in a text editor.
Configure the DuckDB Profile: Replace or add the following configuration under your profile name (istanbul_dbt):
istanbul_dbt:
  outputs:
    dev:
      type: duckdb
      path: C:/Users/PCS/Desktop/Articals/Istunbull-Data/istanbul_database.duckdb
  target: dev
type: Specifies the adapter, which is duckdb.
path: The file path where DuckDB will store the database. Adjust this path as needed.
b. Verify the Configuration Run the following command to test the connection:
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project> dbt debug
You should see a success message indicating that dbt can connect to DuckDB.
Sample Output:
Connections:
  OK connection test to DuckDB database

All checks passed!
5. Loading Your Data into DuckDB
Before working with dbt models, load your CSV data into DuckDB.
a. Create a DuckDB Database and Load Data Open Python Interactive Shell:
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project> python
Execute the Following Python Script:
import duckdb
import pandas as pd

# Connect to the DuckDB database
conn = duckdb.connect('C:/Users/PCS/Desktop/Articals/Istunbull-Data/istanbul_database.duckdb')

# Read the CSV file into a pandas DataFrame
df = pd.read_csv('C:/Users/PCS/Desktop/Articals/Istunbull-Data/customer_shopping_data.csv/customer_shopping_data.csv')

# Create and populate the main table
conn.execute("DROP TABLE IF EXISTS raw_customer_shopping;")
conn.execute("CREATE TABLE raw_customer_shopping AS SELECT * FROM df;")

# Verify the data
result = conn.execute("SELECT * FROM raw_customer_shopping LIMIT 5;").fetchall()
print(result)

# Close the connection
conn.close()
Exit the Python Shell:
>>> exit()
b. Verify Data in DuckDB You can use DuckDB's command-line interface or continue using Python to verify the data. Here's how to verify using Python again:
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project> python
import duckdb

# Connect to DuckDB
conn = duckdb.connect('C:/Users/PCS/Desktop/Articals/Istunbull-Data/istanbul_database.duckdb')

# Query the data
results = conn.execute("SELECT * FROM raw_customer_shopping LIMIT 5;").fetchall()
for row in results:
    print(row)

# Close connection
conn.close()
Expected Output:
('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')
Exit the Python Shell:
>>> exit()
6. Creating dbt Models Based on Your Star Schema
With your data loaded into DuckDB, you can now create dbt models to transform and structure your data according to the star schema.
a. Project Structure Overview
istanbul_dbt_project/
├── istanbul_dbt/
│   ├── models/
│   │   ├── staging/
│   │   │   └── stg_customer_shopping.sql
│   │   ├── marts/
│   │   │   ├── sales.sql
│   │   │   ├── dim_customer.sql
│   │   │   ├── dim_product.sql
│   │   │   ├── dim_date.sql
│   │   │   ├── dim_payment.sql
│   │   │   └── dim_store.sql
│   ├── dbt_project.yml
b. Creating the Staging Model Navigate to the models Directory:
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project> cd istanbul_dbt\models
Create a staging Directory:
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project\models> mkdir staging
Create stg_customer_shopping.sql:
-- models/staging/stg_customer_shopping.sql

WITH raw AS (
    SELECT
        invoice_no,
        customer_id,
        gender,
        age,
        category,
        quantity,
        price,
        payment_method,
        CAST(STRFTIME('%Y-%m-%d', invoice_date) AS DATE) AS invoice_date,
        shopping_mall
    FROM {{ source('raw', 'raw_customer_shopping') }}
)

SELECT
    invoice_no,
    customer_id,
    gender,
    age,
    category,
    quantity,
    price,
    payment_method,
    invoice_date,
    shopping_mall
FROM raw
c. Define Sources in schema.yml Create a schema.yml File:
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project\models> New-Item -Path . -Name schema.yml -ItemType File
Edit schema.yml:
# models/schema.yml

version: 2

sources:
  - name: raw
    tables:
      - name: raw_customer_shopping

models:
  - name: stg_customer_shopping
    description: "Staging model for raw customer shopping data."
    columns:
      - name: invoice_no
        description: "Unique identifier for each transaction."
        tests:
          - not_null
          - unique
      - name: customer_id
        description: "Unique identifier for each customer."
        tests:
          - not_null
      - name: gender
        description: "Customer's gender."
      - name: age
        description: "Customer's age."
      - name: category
        description: "Category of the purchased product."
      - name: quantity
        description: "Quantity of products purchased."
        tests:
          - not_null
      - name: price
        description: "Unit price of the product."
        tests:
          - not_null
      - name: payment_method
        description: "Method of payment."
      - name: invoice_date
        description: "Date of the transaction."
      - name: shopping_mall
        description: "Location of the shopping mall."
d. Creating Dimension Models Create separate SQL files for each dimension and the fact table in the marts directory.
Create a marts Directory:
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project\models> mkdir marts
Navigate to marts:
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project\models> cd marts
Customer Dimension (dim_customer.sql):
-- models/marts/dim_customer.sql

WITH staging AS (
    SELECT
        DISTINCT
        customer_id,
        gender,
        age
    FROM {{ ref('stg_customer_shopping') }}
)

SELECT
    ROW_NUMBER() OVER (ORDER BY customer_id) AS customer_key,
    customer_id,
    gender,
    age
FROM staging
Product Dimension (dim_product.sql):
-- models/marts/dim_product.sql

WITH staging AS (
    SELECT
        DISTINCT
        category
    FROM {{ ref('stg_customer_shopping') }}
)

SELECT
    ROW_NUMBER() OVER (ORDER BY category) AS product_key,
    ROW_NUMBER() OVER (ORDER BY category) AS category_id,
    category AS category_name
FROM staging
Date Dimension (dim_date.sql):
-- models/marts/dim_date.sql

WITH staging AS (
    SELECT
        DISTINCT invoice_date
    FROM {{ ref('stg_customer_shopping') }}
)

SELECT
    ROW_NUMBER() OVER (ORDER BY invoice_date) AS date_key,
    invoice_date,
    STRFTIME(invoice_date, '%Y') AS year,
    STRFTIME(invoice_date, '%m') AS month,
    STRFTIME(invoice_date, '%d') AS day,
    STRFTIME(invoice_date, '%A') AS day_of_week,
    CASE
        WHEN STRFTIME(invoice_date, '%m') IN ('01', '04', '07', '10') THEN 1
        WHEN STRFTIME(invoice_date, '%m') IN ('02', '05', '08', '11') THEN 2
        WHEN STRFTIME(invoice_date, '%m') IN ('03', '06', '09', '12') THEN 3
    END AS quarter,
    CASE
        WHEN STRFTIME(invoice_date, '%w') IN ('0', '6') THEN TRUE
        ELSE FALSE
    END AS is_weekend,
    FALSE AS is_holiday
FROM staging
Note: For is_holiday, you might need to create a more comprehensive list of holiday dates or use an external dataset.
Payment Dimension (dim_payment.sql):
-- models/marts/dim_payment.sql

WITH staging AS (
    SELECT
        DISTINCT payment_method
    FROM {{ ref('stg_customer_shopping') }}
)

SELECT
    ROW_NUMBER() OVER (ORDER BY payment_method) AS payment_key,
    payment_method
FROM staging
Store Dimension (dim_store.sql):
-- models/marts/dim_store.sql

WITH staging AS (
    SELECT
        DISTINCT shopping_mall,
        'Location Placeholder' AS location,  -- Update with actual location data if available
        'Mall Type Placeholder' AS mall_type    -- Update with actual mall type data if available
    FROM {{ ref('stg_customer_shopping') }}
)

SELECT
    ROW_NUMBER() OVER (ORDER BY shopping_mall) AS store_key,
    shopping_mall,
    location,
    mall_type
FROM staging
Note: Replace 'Location Placeholder' and 'Mall Type Placeholder' with actual data or additional transformations if available.
Create Fact Table (fact_sales.sql):
-- models/marts/fact_sales.sql

SELECT
    ROW_NUMBER() OVER (ORDER BY stg.invoice_no) AS sale_id,
    stg.invoice_no,
    dim_customer.customer_key,
    dim_product.product_key,
    dim_date.date_key,
    dim_payment.payment_key,
    dim_store.store_key,
    stg.quantity,
    stg.price,
    stg.quantity * stg.price AS total_amount
FROM {{ ref('stg_customer_shopping') }} AS stg
LEFT JOIN {{ ref('dim_customer') }} AS dim_customer
    ON stg.customer_id = dim_customer.customer_id
LEFT JOIN {{ ref('dim_product') }} AS dim_product
    ON stg.category = dim_product.category_name
LEFT JOIN {{ ref('dim_date') }} AS dim_date
    ON stg.invoice_date = dim_date.invoice_date
LEFT JOIN {{ ref('dim_payment') }} AS dim_payment
    ON stg.payment_method = dim_payment.payment_method
LEFT JOIN {{ ref('dim_store') }} AS dim_store
    ON stg.shopping_mall = dim_store.shopping_mall
e. Update dbt_project.yml Ensure your dbt_project.yml reflects the correct paths and configurations.
# dbt_project.yml

name: istanbul_dbt
version: '1.0'
config-version: 2

# Define the directory where your models are stored
model-paths: ["models"]

# Define the target profiles
profile: istanbul_dbt

# Optional: Configure materializations or other settings
7. Running and Testing Your dbt Project
a. Run dbt Models Navigate back to your project root and execute dbt commands.
Navigate to Project Root:
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project\models> cd ..
Run dbt Models:
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\istanbul_dbt_project> dbt run

“
But i get this error 
(venv) PS C:\Users\PCS\Desktop\Articals\Istunbull-Data\customer_shopping_data.csv\istanbul_dbt_project> dbt run
15:03:43  Running with dbt=1.8.8
15:03:43  Encountered an error:
Runtime Error
  No dbt_project.yml found at expected path C:\Users\PCS\Desktop\Articals\Istunbull-Data\customer_shopping_data.csv\istanbul_dbt_project\dbt_project.yml
  Verify that each entry within packages.yml (and their transitive dependencies) contains a file named dbt_project.yml


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