Demystifying Data Flow: ETL and ELT Explained Simply

Uncover the Key Differences and Pick The Right Approach for Your Next Data Product

You're building a new data product. Should you pick ETL or ELT for your data pipelines? Don't worry if you're unsure—I've got you covered!

Many data professionals struggle with these concepts, but mastering them is crucial for effective data management and analysis.

In this two-part series, I'll explain ETL and ELT in a clear and understandable way. By the end of this article, you'll have a clear grasp of these essential data processing methods and know precisely when to use each one.

In Part One, we'll cover the basics:

• What ETL and ELT actually mean

• The key differences between the two approaches

• Real-world use cases for both ETL and ELT

For part two, I have prepared an extensive comparison of these two techniques.

Let's get started!

Reading time: 8 minutes

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📖 What's the Difference? ETL vs ELT Explained

ETL and ELT are ways to move and process data, but they do it differently. Let me break it down for you.

ETL stands for Extract, Transform, Load. ETL works in three steps:

1. Extract: You pull data from different places like databases, APIs, spreadsheets, or log files.

2. Transform: You clean up the data and get it in the right shape for your needs.

3. Load: You put the cleaned-up data into the data warehouse.

Now, let's look at ELT: Extract, Load, Transform. The steps are the same, but the order is different: