What are the main differences between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform)?

ETL transforms data in a staging layer before loading it into the target warehouse, while ELT loads raw data first and leverages the warehouse’s compute engine for transformation.

Process comparison

1. ETL
1. Extract → source (e.g., MySQL, S3) using sqoop or AWS DMS.
2. Transform → staging server (e.g., Apache Spark, Talend) applying cleansing, type casts, and business rules.
3. Load → target (e.g., Snowflake, Redshift) via bulk copy (COPY INTO).

2. ELT
1. Extract → raw landing zone (e.g., Azure Blob, GCS) with aws s3 cp or bcp.
2. Load → warehouse (Snowflake, BigQuery) using native bulk loaders.
3. Transform → in‑warehouse SQL or procedural scripts (SELECT … INTO …).

Side‑by‑side table

| Aspect | ETL | ELT |
|-------------------|----------------------------------|----------------------------------|
| Compute location | External (Spark, Flink) | Warehouse (Snowflake, BigQuery) |
| Latency | Higher (extra staging) | Lower (direct load) |
| Scalability | Limited by staging cluster size | Scales with warehouse elasticity |
| Typical use case | Complex data quality pipelines | Cloud‑native analytics, ad‑hoc |
| Tool examples | Informatica PowerCenter, Talend | Snowflake Streams, dbt, BigQuery |

Decision checklist
- Do you need heavy data‑cleansing before any analytics? → ETL.
- Is your warehouse capable of handling CPU‑intensive joins (e.g., Snowflake with X‑Large warehouse)? → ELT.
- Are you processing > 10 TB per day and want to avoid staging storage costs? → ELT.
- Do regulatory rules require data to be masked before it touches the warehouse? → ETL.

Sample ELT transformation in Snowflake

CREATE OR REPLACE TABLE sales_clean AS
SELECT
    TO_DATE(order_ts) AS order_date,
    CASE WHEN amount < 0 THEN 0 ELSE amount END AS amount,
    UPPER(customer_state) AS state
FROM raw.sales
WHERE order_ts IS NOT NULL;

Sample ETL job using Apache Spark (Python)

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("ETL").getOrCreate()
df = spark.read.format("jdbc").option("url","jdbc:mysql://...").load()
clean = df.filter(df.amount > 0).withColumn("state", F.upper(df.state))
clean.write.format("snowflake").options(**sf_options).mode("overwrite").save()