Why should you care about ETL pipelines?
What is an ETL pipeline?
ETL stands for Extract, Transform, Load. An ETL pipeline is a procedure of extracting data from one or more sources, transforming it based on the requirements, and loading it into the destination system(s). For example, ETL can combine company information with its transactional data residing in different source systems and store this new information onto S3 as a destination system. Without an ETL pipeline, the data is extracted from various source systems and then stored in an intermediate storage system for performing the transformation, which can be loaded to a destination system. This makes the traditional ETL process slow and complicated.
Why are ETL pipelines important?
The primary purpose of an ETL pipeline is to make data available for different purposes such as data analysis, running a business intelligence system, training machine learning models, building data warehouses, data lakes, and many more. Ultimately the goal is to draw helpful business insights or provide services to a customer. All these systems are only useful if the data they process is precisely what these systems are expecting. If the received data is not what they expect, this will lead to wrong inferences. ETL pipelines can help overcome this problem by always performing the pre-defined steps automatically as a part of the ETL process.
This is one of many benefits that using an ETL pipeline brings. In addition to these, ETL is helpful in:
- Saving us from spending significant time on preparing and extracting information from data
- Keeping the quality of data high
- Providing data reliably.
- Moving from legacy systems to more scalable systems.
- Handling big data.
- Satisfying diverse data demands by different teams within the organization.
- Speeding up data-hungry projects.
- Complying with data privacy laws like GDPR (General Data Protection Regulation), no data is being handled manually.
- Creating a common data repository.
Where are ETL pipelines used in ML projects?
As mentioned earlier, one of the main goals of ETL pipelines is to produce data after several ETL-driven steps. Machine learning projects require data as input. To have a good-performing machine learning model, clean and meaningful data is the key. Depending on the model, some essential steps, such as data cleansing and data reformatting, can be implemented as a part of the ETL pipeline. Thus, making the handling of the data straightforward.
I hope this post has given you a more concrete understanding of ETL pipelines. Now you know about the fundamentals of why ETL pipelines are important. In our next post, you will learn how to orchestrate ETL pipelines to process never stopping incoming data as soon as it is available automatically.
