In my last post, we looked at how databases store information. But how does that data actually get there? As a data engineer, most of your time is spent designing the "pipelines" that move data from source to destination.
Two main methodologies dominate this space: ETL and ELT. These define whether data is transformed before or after it hits your target system. Let's break down the evolution.
What are ETL and ELT?
Both acronyms represent three core steps: Extract, Transform, and Load. The difference lies entirely in the sequence and where the heavy lifting happens.
1. ETL (Extract, Transform, Load)
This is the traditional approach. Data is extracted, transformed in a separate processing layer, and then loaded into the target system.
- Workflow: Data moves from Source → Transformation Engine → Target.
- Strengths: Ensures high data quality and security (masking) before the data is stored.
- Best for: Complex transformations or when target systems have limited resources.
2. ELT (Extract, Load, Transform)
ELT is the modern, cloud-native approach. Raw data is loaded directly into the target system, and transformations are performed using the target's own computational power.
- Workflow: Data moves from Source → Target → Transformation.
- Strengths: Faster loading times and high scalability using cloud warehouses like Snowflake or BigQuery.
- Best for: Big Data scenarios and agile analytics where requirements change rapidly.
Comparison Matrix
| Aspect | ETL | ELT |
|---|---|---|
| Processing Location | External transformation engine | Within target system |
| Data Quality | High (pre-loading validation) | Variable (post-loading validation) |
| Flexibility | Lower (rigid schemas) | Higher (on-demand views) |
| Maintenance | Complex schema management | Easier to adapt to changes |
Modern Architectures: The Medallion Approach
Many modern data teams use a "Hybrid" or Medallion Architecture to balance both worlds. This organizes data into layers:
- Bronze (Raw): The ELT starting point. Raw data is dumped here exactly as it came from the source.
- Silver (Filtered): Data is cleaned, standardized, and joined.
- Gold (Business-Ready): Highly transformed and aggregated data ready for analytics.
Example: Transformation in Action
In an ETL workflow, you might use Python to clean data before loading it:
import pandas as pd
df = pd.read_csv("raw_sales_data.csv")
df['total_price'] = df['quantity'] * df['unit_price']
df['order_date'] = pd.to_datetime(df['order_date'])
df_cleaned = df.dropna()
df_cleaned.to_sql("sales_table", engine)
In an ELT workflow, you load the raw data first and then use SQL (often managed by tools like dbt) inside your warehouse:
CREATE TABLE analytics.fact_sales AS
SELECT
order_id,
quantity * unit_price AS total_price,
CAST(order_date AS DATE) AS order_date
FROM raw_data.sales_staging
WHERE order_id IS NOT NULL;
Conclusion: Which should you choose?
The choice depends on your infrastructure and speed requirements.
- Use ETL if you have strict regulatory compliance, need to mask data before storage, or have limited target resources.
- Use ELT if you are working with cloud-native architectures (BigQuery, Redshift) and need to provide near real-time insights for big data.
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