Lernen Multi-step Analytical Pipelines with CTEs

Abschnitt 1. Kapitel 11

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When you need to build complex analytical reports, breaking your SQL logic into modular, readable steps is essential. Common Table Expressions (CTEs), introduced with the WITH clause, let you organize multi-step data transformations in a way that is both easy to debug and maintain. By chaining multiple CTEs, you can create analytical pipelines where each step builds on the results of the previous one. This approach is especially helpful for tasks such as data cleaning, filtering, aggregation, and advanced calculations, all within a single, readable query.

Definition

A Common Table Expression (CTE) is a temporary, named result set defined within the execution scope of a single SQL query. CTEs improve query readability and maintainability by allowing you to structure complex logic into sequential building blocks.
Advantages:

Modularizes complex queries for clarity;
Makes debugging and testing individual steps easier;
Enables referencing intermediate results multiple times;
Supports recursive and multi-step analytical logic.


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-- Multi-step analytical pipeline using CTEs
WITH
-- Step 1: Clean and prepare the raw sales data
prepared_sales AS (
    SELECT
        sale_id,
        store_id,
        sale_date,
        category,
        total_amount
    FROM sales
    WHERE sale_date >= '2024-01-01'
),
-- Step 2: Aggregate sales by month and category
monthly_category_sales AS (
    SELECT
        DATE_TRUNC('month', sale_date) AS sales_month,
        category,
        SUM(total_amount) AS total_sales
    FROM prepared_sales
    GROUP BY sales_month, category
)
-- Step 3: Final report
SELECT
    sales_month,
    category,
    total_sales
FROM monthly_category_sales
ORDER BY sales_month, category;

Suppose the analytics team is asked for a report showing monthly sales growth, but they want to exclude days with unusually high sales spikes (outliers) that could distort the trend. This is a classic case for a multi-step CTE pipeline: first, you filter out outlier days, then aggregate sales by month, and finally calculate the growth rate.


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-- Stepwise monthly sales growth calculation with outlier removal using CTEs
WITH
-- Step 1: Calculate daily sales totals
daily_sales AS (
    SELECT
        sale_date,
        SUM(total_amount) AS total_sales
    FROM sales
    WHERE sale_date >= '2024-01-01'
    GROUP BY sale_date
),
-- Step 2: Identify outlier days (sales > 95th percentile)
sales_stats AS (
    SELECT
        PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY total_sales) AS p95
    FROM daily_sales
),
filtered_sales AS (
    SELECT
        d.sale_date,
        d.total_sales
    FROM daily_sales d
    CROSS JOIN sales_stats s
    WHERE d.total_sales <= s.p95
),
-- Step 3: Aggregate by month
monthly_sales AS (
    SELECT
        DATE_TRUNC('month', sale_date) AS sales_month,
        SUM(total_sales) AS total_sales
    FROM filtered_sales
    GROUP BY sales_month
),
-- Step 4: Calculate month-over-month growth
growth_report AS (
    SELECT
        sales_month,
        total_sales,
        LAG(total_sales) OVER (ORDER BY sales_month) AS prev_month_sales,
        CASE
            WHEN LAG(total_sales) OVER (ORDER BY sales_month) IS NULL THEN NULL
            ELSE ROUND(100.0 * (total_sales - LAG(total_sales) OVER (ORDER BY sales_month)) / LAG(total_sales) OVER (ORDER BY sales_month), 2)
        END AS pct_growth
    FROM monthly_sales
)
SELECT
    sales_month,
    total_sales,
    prev_month_sales,
    pct_growth
FROM growth_report
ORDER BY sales_month;

Aufgabe

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Segment customers into "High Value", "Mid Value", and "Low Value" groups based on their total order spend, and calculate the average order value for each segment using at least two CTEs.

Group customers by their total order spend and assign them to a segment: "High Value" (spend >= 2000), "Mid Value" (spend >= 500), or "Low Value" (spend < 500).
Calculate the number of customers and the total spend for each segment.
Compute the average order value per segment.

Lösung

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Abschnitt 1. Kapitel 11

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