ETL vs ELT: Why the Modern Data Stack is Shifting

The Paradigm Shift

For decades, ETL (Extract, Transform, Load) was the gold standard. You extracted data, cleaned it on a dedicated server, and then loaded it into your expensive warehouse.

But with the rise of cloud data warehouses like Snowflake, BigQuery, and Redshift, the game has changed. Enter ELT (Extract, Load, Transform).

Deep Dive: ETL vs ELT

The Old Way: ETL (Extract, Transform, Load)

In the traditional ETL model, transformations happen before the data reaches the warehouse. This was necessary because storage and compute were expensive.

• Extract: Pull data from sources (Salesforce, SQL Server, Logs).
• Transform: Clean, aggregate, and mask data on a dedicated ETL server (e.g., Informatica, SSIS).
• Load: Write the finished, polished data into the warehouse.

The Downside: If you need a new column or a different aggregation, you have to rebuild the entire pipeline. It's rigid and slow.

The New Way: ELT (Extract, Load, Transform)

In the ELT model, we leverage the massive power of modern cloud warehouses.

• Extract: Pull data from sources.
• Load: Dump the raw data directly into the warehouse (Data Lake / Staging Area).
• Transform: Use SQL to transform the data inside the warehouse.

The Upside: You have access to all your raw data immediately. Transformations are just SQL queries that can be version-controlled and rerun anytime.

Why ELT is Winning

1. Speed to Destination

With ELT, you just dump raw data into the warehouse immediately. No waiting for complex transformations. Analysts get access to raw data instantly.

2. Infinite Scalability

Modern cloud warehouses separate compute from storage. You can spin up a massive cluster to run a transformation in seconds, then shut it down. Old ETL servers couldn't do that.

3. Cost Effectiveness

Storage is cheap (S3, Blob Storage). Computing is on-demand. You only pay for the transformations you actually run, rather than maintaining a 24/7 ETL server farm.

Comparison Table

The Role of dbt (data build tool)

dbt is the hero of the ELT movement. It allows anyone who knows SQL to build production-grade data pipelines, complete with testing and documentation.

A Complex dbt Example

Here's how you might calculate customer lifetime value (CLV) using dbt, combining data from multiple sources:

-- models/marts/finance/fct_customer_ltv.sql

WITH orders AS (
    SELECT * FROM {{ ref('stg_orders') }}
),

payments AS (
    SELECT * FROM {{ ref('stg_payments') }}
),

customers AS (
    SELECT * FROM {{ ref('stg_customers') }}
),

customer_orders AS (
    SELECT
        customer_id,
        MIN(order_date) as first_order_date,
        MAX(order_date) as most_recent_order_date,
        COUNT(order_id) as number_of_orders,
        SUM(amount) as total_lifetime_value
    FROM orders
    LEFT JOIN payments USING (order_id)
    WHERE payments.status = 'success'
    GROUP BY 1
)

SELECT
    customers.customer_id,
    customers.first_name,
    customers.last_name,
    customers.email,
    customer_orders.first_order_date,
    customer_orders.most_recent_order_date,
    COALESCE(customer_orders.number_of_orders, 0) as number_of_orders,
    COALESCE(customer_orders.total_lifetime_value, 0) as total_lifetime_value,
    CASE
        WHEN customer_orders.total_lifetime_value > 1000 THEN 'VIP'
        WHEN customer_orders.total_lifetime_value > 500 THEN 'Regular'
        ELSE 'New'
    END as customer_segment
FROM customers
LEFT JOIN customer_orders USING (customer_id)

When to Stick with ETL?

ELT isn't the answer for everything. You might still need traditional ETL if:

• Compliance (PII/HIPAA): You absolutely cannot store raw sensitive data in the warehouse. You must mask it before loading.
• Legacy Systems: Your destination is an old on-premise database that can't handle heavy transformation loads.
• Streaming: Real-time transformations (though tools like Flink and Spark Streaming are changing this).

Conclusion

If you're still building rigid ETL pipelines in 2025, it's time to switch. The flexibility, power, and cost-effectiveness of ELT combined with dbt is unbeatable for 95% of modern analytics use cases.