Model Anatomy · Episode 12 · 30-second cut · 0:39
ETL vs ELT in 30 seconds: same total, different data kept
Both pipelines end with 7 orders and $1,117.15. ETL cleans first; ELT loads raw and keeps that raw table, customer emails included.
Key moments
What you’ll needPython 3.10+, pandas, DuckDB
What you’ll need
- python.org/downloads →
Python 3.10 or newerRuns the code. Check yours with python3 --version. - code.visualstudio.com →
VS Code or CursorEither works; so does any editor you like. - git-scm.com/downloads →
gitGets the code from GitHub. 
pandas 2.2+Tablespip install "pandas>=2.2"
DuckDB 1.0+SQL on files and tables, no database serverpip install "duckdb>=1.0"
Set it up
- 1Get the code
git clone https://github.com/DayanEbrar0X/data-anatomy.ai.git cd data-anatomy.ai
- 2Make a virtual environmentIt keeps this project's packages separate from the rest of your computer.
python3 -m venv .venv source .venv/bin/activate
- 3Install the packagesrequirements.txt installs every lesson's packages. This one needs only: pandas, DuckDB.
pip install -r requirements.txt # or just this lesson: pip install "pandas>=2.2" "duckdb>=1.0"
- 4Run it
cd data-engineering/12-etl-vs-elt cd src python3 pipeline.py
Project structure12-etl-vs-elt/
Project structure
data-engineering/12-etl-vs-elt/Open on GitHub →
- scripts/
- make_orders.pywrites src/orders.csv (seeded, 8 rows)
- src/
- orders.csvthe raw export: order_id, amount, email
- pipeline.pymainthe code from the video
- report.pyprints what each warehouse holds
- README.mdthe idea, how to run it, a line-by-line walkthrough, things to try
etl_elt.pyOpen on GitHub →
import duckdb, pandas as pd, report
raw = pd.read_csv("orders.csv", dtype=str)
etl, elt = duckdb.connect(), duckdb.connect()
t = raw.drop_duplicates().drop(columns="email")
t.amount = t.amount.str.strip("$").astype(float)
etl.sql("CREATE TABLE orders AS FROM t")
elt.sql("CREATE TABLE raw_orders AS FROM raw")
elt.sql(open("orders.sql").read())
report.show(etl, elt)Transcript
Same data, same total. But one pipeline keeps every email. ETL or ELT. Same letters, different order.
Eight raw orders: a duplicate, text amounts, and emails. ETL transforms first: drop the duplicate and the email, fix the amounts. Then loads only clean rows. ELT loads all eight raw rows first, then SQL cleans them, inside the warehouse.
Run it. Both: seven orders, about 1,117 dollars. But ELT also kept the raw table, emails included. Same total.
Where the T runs decides what lands.
Read the lesson on GitHub →- #etl
- #dataengineering
- #sql
- #dataprivacy
