ETL vs ELT: Which One Should You Use and Why?

Every data pipeline starts with the same three actions: Extract, Load and Transform. The question is: in which order do you do them and does it matter?

Imagine you work at a company and your job is to take sales record from five different systems - a CRM, an e-commerce platform, a warehouse database, a mobile app and a spreadsheet your finance team insists on keeping. Finally combine them into one clean, reliable report every morning.

To do that, you need a data pipeline: a repeatable process that moves data from where it lives to where it's useful. Two major architectural patterns exist for building these pipelines:ETL and ELT- they represents about when and where to clean your data.

What is ETL?

ETL stands for Extract Transform Load.
STEP 1 - EXTRACT
You pull raw data from your source systems. These could be relational databases(MySQL, PostgreSQL), APIs or flat files(CSV,XML). The data arrives in its raw, messy and original form.

STEP 2 - TRANSFORM
Before your data goes anywhere near your data warehouse, you clean and reshape it. You might deduplicate rows, convert date formats from mm/dd/yyyy to ISO 8601, join tables together or apply business logic. This is the most intensive step and it happens outside your warehouse.

STEP 3 - LOAD
Only after the data is clean, you load it into a data warehouse like Oracle, Teradata or SQL Server.

Note: If your data was messy, you clean it before it touched the warehouse.

What is ELT?

ELT stands for Extract Load Transform. Note that Load and Transform have swapped position which reflects a completely different approach.
STEP 1 - EXTRACT
You pull raw data from source systems.

STEP 2 - LOAD
You load the raw, untransformed data directly into your data warehouse like Snowflake, BigQuery or Redshift. You don't clean it first but you store it in the warehouse and process it later.

STEP 3 - TRANSFORM
Transformation happens inside the warehouse using the warehouse's own compute engine. Analysts write SQL our use tools like dbt to define transformations as queries. You can rebuild transformations at any time without going back to the source.

ETL vs ELT

1. Schema

• ETL is schema-on-write i.e. data is transformed before being written into the target system. This ensures data is ready for immediate use to the loading process.

• ELT is schema-on-read i.e. data is transformed after being loaded into the target system. This allows flexibility in loading data and making it available in the future.

2. Data Availability

• In ETL, data availability is difficult as it is subjective to permission granted from the sources where it is extracted to data analyst.

• In ELT, data is already loaded into data warehouse and it is available if the users have access to it.

3. Flexibility

• In ETL, flexibility in extracting additional data and transforming the data in different ways is difficult as we would need to make changes to the entire linear ETL process.

• In ELT, we have flexibility to transform the same data in different ways at the same time since the entire raw data is already present.

4. Scalability

• In ETL, the scalability factor is for the storage during load and for compute engine during transformation. It is faster to scale on-cloud than on-premise as ETL is resource-intensive.

• In ELT, if we use cloud-based ELT model, it is much more easily adaptable to scale without any performance degradation.

5. Storage

• In ETL, the storage required is quite less than in ELT since we are storing only a subset of data post-extraction as well as transformation.

• In ELT, the storage required is quite higher than in ETL since we are storing the all the data extracted from the sources in the data warehouse.

Conclusion

"Which is better: ETL or ELT?" The reality is there is no one-size fits-all answer. It's not about the approach been better than the other but it's about what fits your business needs. Each has its strengths.

• ETL is best for legacy systems, compliance and structured data needs transforming data before storage for efficient future use.

• ELT favors modern analytics, excels with large data volumes, real-time access and scalability, loading raw data first and transforming it as needed.

The right choice depends on your business needs. ETL for structured, compliant data workflows and ELT for flexible, high-volume and real-time data handling. Both methods can complement each other, depending on the complexity of your analytics.

In conclusion, there is no definitive “better” option only what’s best for your specific use case.