Complex reports with 5-10 table joins need optimization. Understanding join order helps debug slow queries. Critical for data warehouse and analytics queries.

Data Warehouse Query Optimization

Analytics team has slow-running reports joining fact tables with multiple dimension tables. Need to optimize join order for better performance.

Strategic join ordering with dimension table filtering and fact table partitioning

-- Optimized data warehouse query

WITH filtered_time AS (

SELECT date_key

FROM dim_date

WHERE year = 2024 AND quarter = 1

),

high_value_customers AS (

SELECT customer_key, customer_name, customer_tier

FROM dim_customer

WHERE customer_tier IN ('Gold', 'Platinum')

),

active_products AS (

SELECT product_key, product_name, category

FROM dim_product

WHERE status = 'Active' AND category = 'Electronics'

)

SELECT

hvc.customer_name,

ap.product_name,

SUM(f.sales_amount) as total_sales,

COUNT(f.transaction_id) as transaction_count

FROM filtered_time ft

JOIN fact_sales f ON ft.date_key = f.date_key

JOIN high_value_customers hvc ON f.customer_key = hvc.customer_key

JOIN active_products ap ON f.product_key = ap.product_key

GROUP BY hvc.customer_key, hvc.customer_name, ap.product_key, ap.product_name

ORDER BY total_sales DESC;

Reduces report runtime from 45 minutes to 3 minutes. Enables real-time analytics and faster business decisions.

All

E-commerce Performance Optimization

Product catalog page loads slowly due to complex joins across products, categories, reviews, and inventory tables.

Optimized join order with strategic filtering and indexing

-- Optimized product catalog query

WITH available_inventory AS (

SELECT product_id, quantity, warehouse_location

FROM inventory