Learn the basics of ETL, how it works, its benefits and challenges, and how to leverage an ETL pipeline for effective big data management
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.
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:
The details of each step will be discussed in the next section.
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.
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.
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:
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.
ETL pipelines are becoming one of the popular options in designing data pipelines for businesses for good reasons. Some of the are the following:
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:
ETL is not the only popular method of data management. There is also ELT and Reverse ETL.
ELT differs from ETL by loading the data first before transforming it: that is, the steps are as follows:
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.
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:
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.
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