Designing Modern Data Pipelines: ETL vs ELT for Performance and Scalability

As data engineers, we often face the dilemma of choosing between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) patterns when designing data pipelines. This decision can significantly impact the performance, scalability, and data quality of our pipelines. In this post, we'll explore the tradeoffs between ETL and ELT using the Discord Engineering Blog RSS feed as a real-world example, and discuss how to design modern data pipelines that balance these competing factors.

Key Takeaways

ETL is suitable for smaller datasets with simple transformations, while ELT is better suited for large datasets with complex transformations.
The choice between ETL and ELT depends on the specific use case, data volume, and transformation complexity.
A hybrid approach that combines elements of both ETL and ELT can provide the best of both worlds.

The Problem

The Discord Engineering Blog RSS feed provides a wealth of information about the latest developments in Discord's engineering efforts. However, extracting, transforming, and loading this data into a usable format can be a challenging task, especially when dealing with large volumes of data. This is where the ETL vs ELT debate comes in, and understanding the tradeoffs between these two approaches is crucial for designing an efficient and scalable data pipeline.

Data and Sources

The Discord Engineering Blog RSS feed is available at https://discord.com/blog/rss.xml. Data accessed on 2026-07-15. We'll use the `feedparser` library to parse the RSS feed and extract the relevant data.

Loading the Data

We'll start by loading the RSS feed data using the `feedparser` library.

import feedparser
feed = feedparser.parse('https://discord.com/blog/rss.xml')
data = feed.entries

Step 1 — Extracting Data

In this step, we'll extract the relevant data from the RSS feed, such as the title, link, and summary of each post.

def extract_data(data):
    extracted_data = []
    for entry in data:
        title = entry.title
        link = entry.link
        summary = entry.summary
        extracted_data.append({'title': title, 'link': link, 'summary': summary})
    return extracted_data

Step 2 — Transforming Data (ETL)

In this step, we'll transform the extracted data into a usable format, such as converting the summary to plain text and removing any HTML tags.

import html
def transform_data(data):
    transformed_data = []
    for entry in data:
        title = entry['title']
        link = entry['link']
        summary = html.unescape(entry['summary'])
        transformed_data.append({'title': title, 'link': link, 'summary': summary})
    return transformed_data

Step 3 — Loading Data (ETL)

In this step, we'll load the transformed data into a database or data storage system, such as a CSV file or a database table.

import csv
def load_data(data):
    with open('discord_blog_data.csv', 'w', newline='') as csvfile:
        fieldnames = ['title', 'link', 'summary']
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()
        for entry in data:
            writer.writerow(entry)

Step 4 — ELT Pattern

In this step, we'll explore the ELT pattern, where we load the data into a database or data storage system first, and then transform it.

import pandas as pd
def load_and_transform_data(data):
    df = pd.DataFrame(data)
    df['summary'] = df['summary'].apply(html.unescape)
    return df

Complete Script

The full runnable script combining all steps:

#!/usr/bin/env python3
import feedparser
import html
import csv
import pandas as pd

def extract_data(data):
    extracted_data = []
    for entry in data:
        title = entry.title
        link = entry.link
        summary = entry.summary
        extracted_data.append({'title': title, 'link': link, 'summary': summary})
    return extracted_data

def transform_data(data):
    transformed_data = []
    for entry in data:
        title = entry['title']
        link = entry['link']
        summary = html.unescape(entry['summary'])
        transformed_data.append({'title': title, 'link': link, 'summary': summary})
    return transformed_data

def load_data(data):
    with open('discord_blog_data.csv', 'w', newline='') as csvfile:
        fieldnames = ['title', 'link', 'summary']
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()
        for entry in data:
            writer.writerow(entry)

def load_and_transform_data(data):
    df = pd.DataFrame(data)
    df['summary'] = df['summary'].apply(html.unescape)
    return df

if __name__ == "__main__":
    feed = feedparser.parse('https://discord.com/blog/rss.xml')
    data = feed.entries
    extracted_data = extract_data(data)
    transformed_data = transform_data(extracted_data)
    load_data(transformed_data)
    elted_data = load_and_transform_data(extracted_data)
    print(elted_data)

Expected Output

The expected output will be a CSV file containing the transformed data, as well as a printed DataFrame containing the ELTed data.

Limitations and Tradeoffs

The ETL approach is suitable for smaller datasets with simple transformations, but can become bottlenecked for larger datasets with complex transformations. The ELT approach, on the other hand, is better suited for large datasets with complex transformations, but requires more storage and computational resources. A hybrid approach that combines elements of both ETL and ELT can provide the best of both worlds.

Frequently Asked Questions

What is the main difference between ETL and ELT?

The main difference between ETL and ELT is the order in which the data is transformed and loaded. In ETL, the data is transformed before being loaded, while in ELT, the data is loaded before being transformed.

When should I use ETL?

You should use ETL when dealing with smaller datasets with simple transformations, or when the transformation process is computationally expensive.

When should I use ELT?

You should use ELT when dealing with large datasets with complex transformations, or when the loading process is computationally expensive.

What I'd Change

In conclusion, the choice between ETL and ELT depends on the specific use case, data volume, and transformation complexity. A hybrid approach that combines elements of both ETL and ELT can provide the best of both worlds. If I were to redesign this pipeline, I would consider using a more scalable data storage system, such as a cloud-based data warehouse, and leveraging distributed computing frameworks, such as Apache Spark, to handle larger datasets and more complex transformations.

Py Data