What is ETL? (Basic Guide + FAQs)

One of the popular tools in big data management for businesses is the ETL pipeline. It is used to transfer data from e-Commerce and marketing platforms to data warehouses used by businesses to store big data. What is ETL? How does it work? What are its benefits and the challenges in using it? Learn more by reading this article.

What is ETL?

As big data is embraced by businesses, there is a growing need to establish a reliable method of handling incoming data before it is analyzed. One popular method is called ETL. ETL stands for Extract, Transform, Load. It is essentially an acronym of the steps in the process. In summary, the process has three steps:

Extract raw data from various sources
Transform raw data to formats suitable to both data warehouses and analysis tools
Load transformed data to data warehouses

The details of each step will be discussed in the next section.

How does ETL work?

As defined in the previous section, ETL has three steps: extract, transform, load. In this section, we will learn more about each step and see how they work.

Extract raw data from various sources

Your business is probably using several e-Commerce and marketing platforms to run its marketing campaigns, ads, and online stores. Most of them offer an API (application programming interface) that can be used to transfer data from the platform to another platform or data warehouses. For example, you can use the API to transfer data from a platform to Google Sheets, as what we have demonstrated in the past tutorials:

If you want to consolidate data from several platforms you use, you need to individually integrate the API for each platform to the data warehouse. This can easily become a complicated matter due to the nature of each platform.

It is best to use a data ingestion platform that can easily integrate different platforms into one. To get a sense of how an efficient data ingestion platform works, you should try Lido. It has the built-in capabilities to import data from various sources without the need for add-ons or custom scripts that may only work for specific cases. In fact, you can even set up your spreadsheet to also do real-time analytics, a key component of a modern data stack.

Transform raw data to formats suitable to both data warehouses and analysis tools

The data from these platforms will come in varying formats and schemas (the manner the data is stored, like what data goes into a column of a table). Some of these formats and schemas may be incompatible with the data warehouse and analysis tools you use. Additionally, it makes it easier to process the stored data if they follow a single format and schema. For that, you need to transform all the raw data into a single format and schema suitable for the data warehouse and the analysis tools.

Some of the processes involved in data transformation are the following:

Data munging - involves smoothening and cleaning incoming data to ensure consistency both in content and format
Initial calculations - after data munging, certain metrics are needed to be calculated real-time both for the dashboards for businesses and for diagnostics
Encryption and other security-related processes - the nature of your business may mean that your data needs to have additional layers of security, such as an encryption system that can be included in the data pipeline.

Load the transformed data to data warehouse

Once you have the extracted data transformed to a format and schema suitable to the data warehouse and analysis tools, you can now store it in a data warehouse.

What are the benefits of using ETL?

ETL pipelines are becoming one of the popular options in designing data pipelines for businesses for good reasons. Some of the are the following:

Can handle big data - the ETL method is meant to streamline big data handling through a clear, step-by-step process.
Easy to maintain and trace - whether you use a dedicated ETL tool or you improvise with different apps and platforms to meet your specific needs, it becomes possible to easily troubleshoot arising problems. If you improvise your ETL pipeline by using different tools, you can swap the existing tool with another one and fit them together.
Allows you to do advanced data profiling - you can fit data profiling tools along the ETL pipeline so that while transforming the data, you can already display preliminary results in a dashboard.
Allows you to track data for quality control - you can fit data quality control tools along the ETL pipeline so you can see in real-time how the data is being processed and whether issues arise along the pipeline.

What are the challenges in using ETL?

While ETL pipelines are becoming popular options for good reasons, every business that wants to use ETL should be mindful of challenges when implementing and using it. Some of them are as follows:

Expertise needed - ETL pipelines can be tricky to set up, especially if you customize it by choosing specific apps for each step.
Situation-dependent - the resulting ETL pipeline depends on the situation, such as the data source, the preprocessing needed, etc. If your needs changed, you might need to change something along the pipeline and may have to completely rebuild it.
May be harder to scale up - if you choose a separate app for each step of the pipeline, they will have their own limits in the amount of data they can handle. Some of these apps may cause bottlenecks in the process.
Slower processing time - Each additional step in your ETL pipeline slows the amount of time it takes for your data to pass through.

ETL vs ELT vs Reverse ETL

ETL is not the only popular method of data management. There is also ELT and Reverse ETL.

Extract, Load, Transform: ELT

ELT differs from ETL by loading the data first before transforming it: that is, the steps are as follows:

Extract raw data from various sources
Load transformed data to data warehouses
Transform raw data to formats suitable to both data warehouses and analysis tools

The main advantage of ELT over ETL is that the data extracted from sources is loaded to data warehouses first before transforming it. This already cuts short the time it takes for the data to pass through the pipeline. It also makes the pipeline easier to maintain and has less potential for bottlenecks to occur.

Reverse ETL

If the data you needed is already stored in data warehouses, you may also need to set up a pipeline to access it. The pipeline you use is the Reverse ETL pipeline. The steps are the following:

Extract data from data warehouse
Transform data to formats suitable for dashboards and analytics tools
Load transformed data to dashboards and analytics tools

Unlike in traditional ETL, the process of transformation occurs inside the data warehouse. Therefore, the data warehouses must also contain the capability to transform the data stored in it.