Understanding ETL/ELT Data Pipelines in Data Engineering
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In the world of data engineering, ETL and ELT are crucial processes that help businesses make sense of their vast amounts of data. These processes are essential for collecting, transforming, and storing data in a way that makes it useful for analysis and decision-making. Let's break down these concepts in simple terms.
What is ETL?
ETL stands for Extract, Transform, Load. It’s a process used to move data from various sources into a data warehouse or another centralized data repository. Here’s a closer look at each step:
1. Extract:
This is the first step, where data is collected from various sources. These sources could be databases, cloud services, applications, or even flat files like CSVs. The goal is to gather all the necessary data, regardless of its format or location.
2. Transform:
Once the data is extracted, it needs to be cleaned and transformed into a format suitable for analysis. This step may involve:
- Removing duplicates
- Handling missing values
