ETL vs ELT Explained
ETL and ELT are the two core patterns for moving data from sources to analytics systems. Learn how they differ, when to use each, and how modern cloud warehouses shifted the industry toward ELT.
What is ETL?
ETL (Extract, Transform, Load) pulls data from source systems, transforms it on a separate processing engine, and then loads the cleaned result into the target.
1. Extract — Read data from databases, APIs, files, or event streams. 2. Transform — Clean, validate, join, and reshape the data. 3. Load — Write the final result into the warehouse or data mart.
What is ELT?
ELT (Extract, Load, Transform) reverses the last two steps: raw data is loaded into the warehouse first, and transformations run inside it using SQL. This leverages the elastic compute of modern cloud warehouses and keeps the raw data available for reprocessing.
ETL vs ELT at a Glance
Key differences between the two approaches:
- Transform location — ETL: external engine (Spark, Python). ELT: inside the warehouse.
- Raw data retention — ETL often discards raw data. ELT stores it for replay.
- Flexibility — ELT lets analysts change transformations without re-extracting.
- Best fit — ETL for strict compliance/masking before load. ELT for cloud-native analytics.
- Typical tools — ETL: Informatica, Talend, Spark. ELT: Fivetran/Airbyte + dbt.
A Minimal ETL Script
Here is a simple Python ETL job that extracts from a CSV, transforms with pandas, and loads into a SQL table:
import pandas as pd
from sqlalchemy import create_engine
# Extract
df = pd.read_csv("orders_raw.csv")
# Transform
df["order_date"] = pd.to_datetime(df["order_date"])
df = df.dropna(subset=["customer_id"])
df["amount"] = df["amount"].astype(float).round(2)
df = df.drop_duplicates(subset=["order_id"])
# Load
engine = create_engine("postgresql://user:pass@localhost:5432/warehouse")
df.to_sql("fact_orders", engine, if_exists="append", index=False)Key Takeaways
- ETL transforms before loading; ELT transforms after loading.
- Cloud warehouses made ELT practical and cost-effective.
- Keeping raw data enables reprocessing when logic changes.
- Choose based on compliance needs, data volume, and team skills.
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