![]() Loading is primarily performed in two ways, depending on the type of pipeline an organization prefers: real-time or batch.īatch loads are scheduled loads that occur every hour, day, or week. Putting everything in a centralized location prepares the data for comparative analysis or allows for more informed decisions with larger datasets. The loading phase takes the transformed data and puts it into a final destination like databases or data warehouses. All of this is meant to push data into useful forms rather than a nonsensical jumble of characters. So, engineers may look for factors like repetitive data points or extreme outliers. Other examples include normalizing abbreviations, rounding decimals, and merging similar categories.īesides making data easier to compare, the cleansing process also aims to remove problematic inputs that would harm the result. kilometers, would be transformed to use the same measuring systems. This step restructures data in ways that make it easier for programs or humans to compare against each other.įor example, data using different measurements, such as miles vs. ![]() Transformation is also known as data cleaning. The goal is to efficiently pull all of the raw data while maintaining its integrity. ![]() The extraction stage may pull from sources such as databases, applications, APIs, spreadsheets, and much more. This is the first step of the ETL process, in which data is collected from various places. There are countless ways to configure and customize ETL to fit your needs, but it always consists of three main stages: Extraction, Transformation, and Loading. Understanding the ETL ProcessĮTL is a fundamental process in many organization's data processes. This post will explore the ETL process, its key components, implementation methods, and the challenges it brings to organizations. This may sound like a simple pipeline, but companies deal with thousands of different data sources, and figuring out how and where to apply their findings is a massive order. ETL is the process of pulling data from many sources and managing it for quick and accurate analysis. This is where Extract, Transform, and Load (ETL) throws itself into play. Collecting, cleaning, and delivering data is crucial for reaching correct conclusions and improving operations and strategy. Everyone's heard some form of the age-old adage, "Information is power." Today, managing data is what gives organizations huge advantages over their competitors.
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