![]() Productivity - it eliminated heavy coding processes, saving both time and money and improves productivity.Interpretable - with more data, we have a consolidated view and can make better interpretations through analysis and reports.Context - organizations have more historical data to provide them with context.When you combine different datasets in a centralized repository, it provides: So why just leave it there to do nothing? We all know what data can do in this day and age - the things it’s been able to create, the problems it has solved, and how it can benefit our future. Meaning that they are in different formats, inconsistent, and do not communicate with other aspects of the business well. Most companies have a lot of data but they tend to be siloed. The benefits it brings to machine learning are that it helps extract data, clean it up and deliver it from Point A to Point B. Once your data is in the correct format, it can be loaded into the target database.Įvery part of the ETL phase is important to deliver the end product accurately. During the TRANSFORM stage, you will clean up the data, search and rectify any duplicates and prepare it to be loaded into another database. Like the majority of the time when working with data and machine learning algorithms - there’s a phase of cleaning it up. This can be located in another database or application overall. Your first step will be to EXTRACT the data from its original source. ![]() It is the process of moving data from multiple sources to bring it to a centralized single database. It is the machine learning algorithms that produce these predicted outputs by learning on historical data and its features.ĮTL stands for Extract-Transform-Load. So what does ETL have to do with machine learning?įor those who don’t already know, machine learning is a type of artificial intelligence that uses data analysis to predict accurate outcomes. You may have heard ETL getting thrown in sentences here and there when you're reading blogs or watching YouTube videos.
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