From Raw Events to Refined Data: ETL vs ELT in Modern Data Pipelines

Should I transform the data before storing it? Or after?

Welcome to the world of ETL vs ELT.

In this blog, let us go through some topics such as:

• What ETL and ELT actually mean (without any jargon!)

• Key differences and trade offs

• Which is better for modern architectures (Hint: it depends!)

• Real world tools used for each

What is ETL?

ETL stands for:

• Extract :Get the raw data from sources (databases, APIs, logs)

• Transform : Clean, aggregate, and structure the data

• Load : Store the final, processed data in a warehouse or database

Example:
You use Python scripts or Apache Spark to clean messy CSV logs, and then push the result into PostgreSQL or BigQuery.

ETL was popular when compute was expensive and storage limited, so you cleaned data first to save space.

What is ELT?

ELT flips the order:

• Extract : Pull raw data from sources

• Load : Dump it directly into a data lake or warehouse (like Snowflake, S3, or BigQuery)

• Transform : Use SQL, dbt, or Spark to clean/process it inside the storage layer

Example:
You dump all your CRM and analytics data into BigQuery, and run SQL/dbt jobs to convert it into dashboards later.

ELT is powerful when you have cheap, scalable cloud storage and fast compute, which is now common.

ETL vs ELT comparison

Real World Tool Examples

Where Does Each Shine?

Choose ETL when:

• You have limited storage

• Strict data quality is needed before loading

• You are using on prem systems or legacy infrastructure

Choose ELT when:

• You're on cloud warehouses (Snowflake, BigQuery, etc.)

• You need raw data preserved

• You want fast iteration with SQL transformations

Hybrid is the New Normal

In practice, most modern architectures blend both approaches:

• Real-time streaming ETL is used for alerts, monitoring, and operational dashboards

• Batch ELT powers reporting, forecasting, machine learning, and long-term storage

Conclusion

ETL and ELT aren’t rivals, instead they’re options in your engineering toolbox.
Your choice depends on your infrastructure, latency needs, storage budget, and team skills.

The best pipelines are adaptable, evolving from ETL to ELT, batch to streaming, and raw events to valuable insights.