As we discussed data integration in the previous section, you learned that it often involves gathering data from different places and making it work together. One of the most established and widely used patterns for achieving this is called ETL, which stands for Extract, Transform, Load. Think of it as a systematic, three-step process for moving data from its origin to a destination where it can be analyzed or used by applications.
Let's break down exactly what happens in each stage:
The first step is Extract. This involves reading and retrieving data from one or more source systems. These sources could be anything where data resides:
The primary goal of the Extract step is simply to get the required data out of its original location. At this stage, the data is often in its raw, unaltered format. We identify the specific data needed and pull it into a processing area, sometimes called a staging area, getting it ready for the next step.
Once the data is extracted, the Transform step begins. This is often the most complex part of the process. Here, the raw data is cleaned, validated, and reshaped to meet the requirements of the target system and the intended use (like analysis or reporting). Common transformation activities include:
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, making sure state abbreviations are consistent).The Transform stage ensures the data becomes consistent, accurate, and suitable for its final destination.
The final step is Load. After the data has been transformed, it needs to be written into the target system. This target is typically a database, a data warehouse, a data lake, or another system designed for analysis or operational use.
Loading can happen in different ways:
The Load step makes the prepared data available to end-users, analysts, data scientists, or applications that need it.
Visually, the process forms a pipeline where data flows sequentially through these three stages:
A diagram illustrating the flow of data from source systems through the Extract, Transform, and Load stages into a target system.
In summary, ETL is a fundamental process in data management. It provides a structured way to:
Understanding these three distinct stages is the first step toward designing and building effective data pipelines. In the following chapters, we will examine each of these stages in greater detail.
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