Why advanced patterns solve complex analytics in dbt - See It in Action

Why advanced patterns solve complex analytics

📖 Scenario: You work as a data analyst in a retail company. You have a large sales dataset and want to analyze customer buying patterns over time. Simple queries are slow and hard to maintain. Your manager asks you to use advanced dbt patterns to build efficient, reusable models that solve complex analytics problems.

🎯 Goal: Build a dbt project that uses advanced patterns like CTEs, incremental models, and macros to analyze customer purchase frequency and segment customers by activity level.

📋 What You'll Learn

Create a base model with raw sales data

Add a config variable to enable incremental loading

Use a CTE and window functions to calculate purchase frequency per customer

Print the final customer segments with counts

💡 Why This Matters

🌍 Real World

Retail companies analyze customer purchase patterns to target marketing and improve sales.

💼 Career

Data analysts and engineers use dbt advanced patterns to build scalable, maintainable analytics pipelines.

1

Create the base sales model

Create a dbt model file called base_sales.sql with a SELECT statement that selects customer_id, order_id, and order_date from the raw_sales table.

dbt

-- Your code here

Need a hint?

This base model selects the raw sales data columns needed for analysis.

2

Add incremental model configuration

In the base_sales.sql model, add a config block to enable incremental loading with unique_key='order_id'.

dbt

select customer_id, order_id, order_date from raw_sales

-- Add config block for incremental model here

Need a hint?

Use the config Jinja function at the top of the model to set incremental materialization.

3

Calculate purchase frequency using CTE and window functions

Create a new model called customer_frequency.sql. Use a CTE named base that selects all columns from ref('base_sales'). Then, in the main query, use a window function COUNT(order_id) OVER (PARTITION BY customer_id) to calculate purchase_count per customer.

dbt

-- Your code here

Need a hint?

Use a CTE to reference the base model and a window function to count orders per customer.

4

Segment customers and output results

In the customer_frequency.sql model, add a final SELECT that uses a CASE statement to create a segment column: 'High' if purchase_count >= 5, 'Medium' if between 2 and 4, else 'Low'. Then, group by segment and count distinct customer_id. Print the results.

dbt

with base as (
    select * from {{ ref('base_sales') }}
)

-- Add segmentation and aggregation here

# Your code here

Need a hint?

Use CASE to segment customers and GROUP BY to count customers per segment.