can anyone explain the difference between ETL and ELT with an example

ETL vs ELT – What the acronyms mean

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Why the difference matters

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Concrete example

Assume we have a transactional system that writes sales data to a MySQL database. We want to build a daily sales dashboard in a Snowflake data warehouse.

ETL pipeline

1. Extract

• Pull the last 24 hours of rows from MySQL using a CDC tool or a scheduled query.
• Store the raw rows in a temporary staging table on a separate ETL server (or a dedicated staging database).

1. Transform

• In the staging server run SQL or a Python script to:
• Convert timestamps to UTC.
• Normalize product names.
• Aggregate sales by product and region.
• Add calculated fields (e.g., discount_amount = price * discount_rate).

1. Load

• Push the cleaned, aggregated rows into the sales_daily table in Snowflake via a bulk load (e.g., COPY INTO from a CSV file stored in S3).

Result: Snowflake contains only the final, ready‑to‑query data. The raw MySQL data never resides in Snowflake.

ELT pipeline

1. Extract

• Pull the last 24 hours of rows from MySQL, but this time write them directly to a staging table in Snowflake (e.g., sales_raw).

1. Load

• The raw rows are now in Snowflake. No transformation yet.

1. Transform

• Run a Snowflake SQL script that:
• Creates a view or materialized table sales_daily from sales_raw.
• Performs the same cleaning, normalization, aggregation, and calculations as above, but inside Snowflake.

Result: Snowflake holds both the raw and the transformed data. All heavy lifting (parallel scans, distributed joins) is done by Snowflake’s compute engine.

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When to choose which

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Quick checklist for building an ELT pipeline

1. Extract → Load raw data into a staging table in the warehouse.
2. Validate → Run basic checks (null counts, row counts) on the raw table.
3. Transform → Use SQL, UDFs, or native warehouse functions to clean and aggregate.
4. Materialize → Create permanent tables or views for downstream consumption.
5. Govern → Add metadata, lineage, and access controls in the warehouse.

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TL;DR

• ETL moves data out of the source, transforms it in a separate environment, then loads the clean data into the target.
• ELT loads the raw data into the target first, then uses the target’s compute power to transform it.

Choose based on where you want the heavy lifting to happen and how much compute you can afford to dedicate to the ETL step.