Architecture & Query Optimization

Architecture & Query Optimization

** Architecture & Query Optimization** is a foundational topic for data engineers building scalable analytics platforms.

Why It Matters

Modern data warehouses handle petabytes of data and serve thousands of analysts. Designing them well is both an art and a science.

Architecture Overview

-- Example: Star schema fact table
CREATE TABLE fact_orders (
    order_id        BIGINT PRIMARY KEY,
    customer_key    INT REFERENCES dim_customer(customer_key),
    product_key     INT REFERENCES dim_product(product_key),
    date_key        INT REFERENCES dim_date(date_key),
    quantity        INT,
    unit_price      DECIMAL(10,2),
    total_amount    DECIMAL(10,2),
    created_at      TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
PARTITION BY RANGE (date_key);

-- Dimension table
CREATE TABLE dim_customer (
    customer_key    INT PRIMARY KEY,
    customer_id     VARCHAR(50) UNIQUE,
    full_name       VARCHAR(200),
    email           VARCHAR(200),
    country         VARCHAR(100),
    segment         VARCHAR(50),
    valid_from      DATE,
    valid_to        DATE,
    is_current      BOOLEAN DEFAULT TRUE
);

Key Design Principles

• Choose the right grain for fact tables
• Use surrogate keys for all dimensions
• Implement Slowly Changing Dimensions (SCD) appropriately
• Partition data for query performance

Summary

Good warehouse design directly impacts query performance, cost, and developer productivity.