What is the purpose of ETL?

ETL helps prepare raw data for analysis by extracting it from the source, transforming it into usable formats, and loading it into a system where it can be queried efficiently.

Can I build an ETL pipeline using Python only?

Yes! For smaller or medium pipelines, Python with libraries like Pandas, requests, and SQLAlchemy is powerful and flexible.

Is ETL still relevant in 2025?

Absolutely. While tools like ELT (e.g. dbt) and real-time streaming exist, ETL remains critical for batch processing, compliance, and data quality control.

What is ETL? A Beginner’s Guide with Python Examples

ETL stands for Extract, Transform, Load, a fundamental process in the field of data engineering. It’s the pipeline that moves raw data from its source, cleans and transforms it, and stores it where it can be analyzed.

In this post, you’ll learn:

What ETL means
Why it’s important in the data world
How to build a basic ETL pipeline using Python and Pandas
Where to go from here to level up your data engineering skills

What is ETL?

ETL stands for:

Extract: Pull data from a source (e.g. an API, database, or CSV file)
Transform: Clean, reformat, or enrich the data
Load: Move the transformed data into a target system like a database

Think of ETL like making coffee:

You extract the coffee beans from the bag,
You transform them by grinding and brewing,
Then you load the coffee into a cup, ready to consume.

Why ETL Matters in Data Engineering

ETL is the glue that connects raw data to valuable insights. Companies use ETL pipelines to:

Prepare data for dashboards and reports
Feed data into machine learning models
Migrate and clean data from multiple sources
Automate daily or hourly updates for real-time decision-making

Without a solid ETL pipeline, even the most advanced analytics tools are useless.

Common ETL Tools (Beyond Python)

While Python is great for learning and prototyping ETL pipelines, larger systems often use:

Apache Airflow – for orchestration/scheduling
dbt – for SQL-based data transformations
AWS Glue – managed cloud ETL
Talend, Informatica – enterprise ETL suites

Building a Simple ETL Pipeline in Python

Let’s build a mini ETL pipeline using real-world data: we’ll pull COVID-19 stats from an API, clean the data, and save it to a local SQLite database.

Step 1: Extract – Pull Data from an API

import requests
import pandas as pd

# Extract: Fetch COVID-19 data from an open API
url = "https://api.covid19api.com/dayone/country/us"
response = requests.get(url)
data = response.json()

# Convert to DataFrame
df = pd.DataFrame(data)
print(df.head())

Step 2: Transform – Clean the Data

# Keep only relevant columns
df_clean = df[["Date", "Confirmed", "Deaths", "Recovered"]].copy()

# Convert date to proper datetime format
df_clean["Date"] = pd.to_datetime(df_clean["Date"])

# Handle missing values
df_clean.fillna(0, inplace=True)

print(df_clean.tail())

Step 3: Load – Save to a Local SQLite Database

import sqlite3

# Connect to SQLite database (or create it)
conn = sqlite3.connect("covid_data.db")

# Load: Write DataFrame to a SQL table
df_clean.to_sql("us_covid_stats", conn, if_exists="replace", index=False)

print("Data loaded into SQLite database successfully.")
conn.close()

Putting It All Together

Here’s what the full ETL flow looks like:

EXTRACT (API) → TRANSFORM (Pandas) → LOAD (SQLite)

You can wrap this into a script or DAG to run daily using a tool like Apache Airflow, cron, or Prefect.

Real-World Next Steps

Once you understand this basic flow, you’re ready to scale up by:

Replacing SQLite with PostgreSQL, BigQuery, or S3
Scheduling the pipeline with Airflow
containerizing your ETL with Docker
Monitoring with Prometheus or logs

Final Thoughts

ETL is the foundation of modern data infrastructure. Whether you’re cleaning CSVs, syncing APIs, or feeding machine learning pipelines, it all starts with ETL.

Want to go further? Check out my other articles on data engineering concepts.

Frequently Asked Questions

Q: What is the purpose of ETL?: A: ETL helps prepare raw data for analysis by extracting it from the source, transforming it into usable formats, and loading it into a system where it can be queried efficiently.
Q: Can I build an ETL pipeline using Python only?: A: Yes! For smaller or medium pipelines, Python with libraries like Pandas, requests, and SQLAlchemy is powerful and flexible.
Q: Is ETL still relevant in 2025?: A: Absolutely. While tools like ELT (e.g. dbt) and real-time streaming exist, ETL remains critical for batch processing, compliance, and data quality control.

What is ETL? A Beginner’s Guide with Python Examples