How to ensure data quality while designing an ETL?

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 How to ensure data quality while designing an ETL? 

 

While designing the ETL Process, “How to ensure data quality” is the most asked Question? 

And if you’re looking for an answer, then you’re at the right place because in this article, we’ve discussed the ETL process and how to ensure its data quality.  

There are some key points you need to consider while designing your ETL process to ensure data quality. And we’ve covered in-depth details on each point. 

As we all know that ETL stands for Extraction, Transformation & Loading processes, and to ensure data quality, you need to consider some points while designing ETL. 

Without wasting a second, Let’s Begin! 

Here are some key points we’ve discussed below:

#1 Data Load & Record Count

The number of records loaded in the target must equal the number of records available in the source. That’s why record count is an essential aspect of ensuring quality.  

 

#2 Data Type 

Whatever the data available in the source, it should be loaded in the target. Then, according to your business, you can do the type conversion.  

Example – Date should be loaded as the date in the target. Because if you load strings in the latest stage and want to make a date comparison, then it’s pretty tough for you. That’s why you need a data type to ensure data quality. 

#3 Precision 

Let’s suppose the amount of source is 28.8 cents, and you’ve loaded as $25 only then you never reconcile your amounts. If you’re designing an ETL process, then it’s helpful for you.  

 

 

#4 Consistency of the Precision 

When you’re designing an ETL process, you need to consider precision. For example, if it’s two digits, then it should be two digits across the system. In precision, inconsistency is a huge problem you’ll face while doing reconciliation or data validation.  

 

#5 About the Duplicates 

Make sure, in the target, there’s no duplicate. Let’s explain with an example. 

If the amount of $25.8 you loaded two times in the target system, it’s sum up to $51.6, and this can be a significant gap in amount matching. To ensure the data quality, you need to keep in mind that there are no Duplicates available. 

But if you see any Duplicates in Incremental, you should make a process for rejection, Insertion, or updating in the target. 

#6 Your Data, Your ETL Should Always have the Surrogate Key 

These are the auto implement keys. When you want to apply the patch or delete/update any data, surrogate keys can help do it. 

#7 Lock File 

At each step, whenever you’re designing ETL, you should lock that in the error file. So that if any failure happens in the process, you can quickly resolve it by seeing lock files. You need to make sure that you’re locking each step of your ETL. 

So, if you’re designing ETL, then these are the key points you should consider in your mind. First, it helps you to ensure data quality. 

Final Verdict 

Finally, we’ve discussed all the key points that help you to ensure the quality of ETL. So make sure you keep in mind these Points and design your ETL Process.