ETL Pipeline from Scratch - Should You Roll Your Own?

Ah, ETL – the magical process of Extracting data from one place, Transforming it into something useful, and Loading it into a final destination. Sounds easy, right?

Well, in theory, yes. In practice? It’s a bit like saying, “I’m going to build my own house because hammers exist.”

So, should you roll your own ETL pipeline from scratch, or should you use tools like Apache NiFi, Talend, Spark, AWS Glue, or DBT?

Let’s break it down.

A Simple ETL Pipeline from Scratch

Before we talk about the pros and cons of DIY ETL, let’s look at what a very simple ETL pipeline might look like in Python.

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import pandas as pd
import sqlite3

def extract():
    # Simulate extracting data from a CSV file
    return pd.read_csv("data.csv")

def transform(df):
    # Simple transformation: clean column names and drop duplicates
    df.columns = [col.strip().lower() for col in df.columns]
    return df.drop_duplicates()

def load(df, db_path="database.db"):
    with sqlite3.connect(db_path) as conn:
        df.to_sql("cleaned_data", conn, if_exists="replace", index=False)

# Run the ETL pipeline
if __name__ == "__main__":
    data = extract()
    cleaned_data = transform(data)
    load(cleaned_data)

What This Pipeline Does

• Extracts data from a CSV file.
• Transforms it by cleaning up column names and removing duplicates.
• Loads it into a SQLite database.

Pretty straightforward, right?

Now let’s compare this with tools that already exist.

DIY ETL vs. The Big Players

When Does Rolling Your Own Make Sense?

You Should Roll Your Own If:

• You have simple ETL needs and don’t want to introduce heavy tools.
• Your budget is $0, and you don’t mind spending time on maintenance.
• You need a custom solution that existing tools can’t handle.
• You enjoy suffering. (Kidding. Kind of.)

You Should NOT Roll Your Own If:

• Your data volume is growing and scalability matters.
• You need real-time processing (NiFi, Spark, Glue are better for this).
• You want low maintenance (AWS Glue or DBT might be your friend).
• You don’t want to reinvent the wheel.

Costs to Maintain and Extend

Rolling your own ETL pipeline starts cheap but gets expensive fast. Here’s why:

• Maintenance Costs: You have to handle logging, monitoring, failure recovery, and scaling yourself.
• Tech Debt: Over time, your DIY pipeline will accumulate weird edge cases that become harder to manage.
• Developer Time: Instead of focusing on business insights, you’ll spend time debugging pipeline failures at 3 AM.

Using managed solutions like AWS Glue or DBT eliminates a lot of these concerns.

Final Thoughts

If you’re just cleaning up a few CSVs, rolling your own ETL pipeline makes sense.

But if you’re building a production-grade pipeline, think twice before reinventing the wheel. Tools like Apache NiFi, Talend, Spark, AWS Glue, and DBT exist for a reason – they handle the dirty work so you don’t have to.

So, should you roll your own ETL pipeline?

Probably not. Unless you really, really love writing ETL code.

🔑 Key Ideas

🔗 References

1. Apache NiFi
2. Talend
3. Apache Spark
4. AWS Glue
5. DBT