Migrating from On-Premise to Cloud: Why Shifting from ETL to ELT is a Smart Data Strategy.

When I started my career, I worked on an ETL migration project, and I thought I’d share some insights with you. Recently, I decided to revisit ETL migration in more depth and discovered an interesting trend: moving from on-premises systems to the cloud often results in a shift from ETL to ELT.

While switching from ETL to ELT isn’t mandatory, it’s often recommended due to its advantages, such as greater scalability, cost efficiency, and improved compatibility with modern data warehouses.

Let me first explain the basics of ETL and ELT processes.

What is Extract, Transform, Load (ETL)?

ETL stands for Extract, Transform, Load, and is the process of extracting business data from various data sources, cleaning and transforming it into a format that can be easily understood, used, and analyzed, and then loading it into a destination or target system so that we can send to the downstream uses such as business intelligence (BI) and big data analytics.

I have uploaded the ETL code to showcase the complete flow of extracting, transforming, and loading data from a single source. Please check it out here: ETL Processes Code

The diagram below illustrates the process of extracting, transforming, and loading data from a single source, such as a CSV.

The diagram below illustrates the process of extracting, transforming, and loading data from multiple sources.

What is Extract Load Transform (ELT)?

ELT stands for “Extract, Load, and Transform” and describes the set of data integration processes to extract data from one system, load it into a target repository, and then transform it for downstream uses such as business intelligence (BI) and big data analytics.

The diagram below illustrates the process of extracting data from multiple sources.

The migration from on-premises to the cloud should also trigger a switch from ETL to ELT?

When migrating from on-premises to the cloud, you may wonder if you should switch from ETL (Extract, Transform, Load) to ELT (Extract, Load, Transform). Let’s break down both ETL and ELT in the context of on-premises and cloud systems and understand why moving to ELT during cloud migration makes sense.

On-Premises ETL:

On-premises ETL refers to data integration and processing that happens within a company’s local servers or data centers. In this model, data is extracted, transformed, and then loaded into a local database or data warehouse, such as Oracle Data Warehouse or Microsoft SQL Server.

Cloud ETL:

Cloud ETL, on the other hand, happens in cloud-based platforms. The data is extracted, transformed, and then loaded into cloud databases like Amazon Redshift, Google BigQuery, or Snowflake. With cloud ETL, you still do the transformations outside of the cloud database, which can limit some of the benefits of the cloud.

Can ETL Still Be Used When Migrating to the Cloud?

Yes, it is possible to continue using ETL when moving to the cloud, but it comes with some challenges:

1. Legacy Systems: If an organization has tightly integrated legacy systems, it might be easier to migrate the existing ETL process to the cloud to avoid a complete redesign.
2. Limited Use of Cloud Features: Continuing with ETL doesn’t fully take advantage of the cloud’s power, like the ability to scale and perform transformations directly in the cloud database.
3. More Complexity: ETL processes in the cloud require additional resources for transformations outside the database, adding to costs and complexity.

Why Switch from ETL to ELT During Cloud Migration?

As organizations move to the cloud, switching from ETL to ELT becomes a smart choice. Here's why:

1. Cloud-Native Features: Cloud data warehouses like Snowflake, BigQuery, and Amazon Redshift are designed to handle transformations directly within the database. With ELT, you can: Maximize Cloud Compute Power: ELT takes advantage of the cloud’s distributed computing power, which makes data transformations faster and more efficient. Process Data in Parallel: Cloud platforms are optimized for processing large amounts of data at once, making transformations happen quicker.
2. Scalability and Flexibility: ELT: You load the raw data first, and then transform it as needed. This is flexible because you can transform the same raw data multiple times for different purposes without reloading it. ETL: You pre-transform the data, so if new requirements come up, you need to re-extract and re-transform the data, which is less efficient.
3. Simplified Architecture: ELT: You don’t need separate ETL tools and storage for transformed data. Everything can happen within the cloud database, which reduces complexity and operational overhead. ETL: Requires managing multiple systems, including the ETL tool and the database, which can be inefficient in the cloud environment.
4. Cost Efficiency: ELT: Raw data is stored in the cloud warehouse, which is usually cheaper for storage. Transformations are done only when needed, saving resources. ETL: The process involves transferring data between multiple tools, which can increase data transfer costs, especially when dealing with large amounts of data.

Conclusion

Switching from ETL to ELT during a cloud migration is often recommended because ELT takes full advantage of cloud infrastructure. It simplifies processes, reduces costs, and enhances performance, making it a more efficient and scalable approach in the cloud environment.