What is a staging area?

· 3 min read

1. Introduction

Working with data pipelines, you might have noticed a staging area in most data pipelines. If you work in the data space and have questions like

Why is there a staging area? Can’t we just load data into the destination tables?

Why do we need to store raw/unprocessed data if we already have the cleaned data?

Isn’t it expensive to store data that we are not going to use?

Is the data removed from the staging area completely, once the transformation has been done?

Then this post is for you. In this post, we will go over what exactly a staging area is and why it is crucial for data pipelines.

2. What is a staging area

A staging area is a data pipeline design concept. It refers to an area where the raw/unprocessed data lives, before being transformed for downstream use. Staging areas can be database tables, files in a cloud storage system, etc.

3. The advantages of having a staging area

In most data pipelines, data in the source system is constantly changing. Storing the data from the source in a staging area with every run of the data pipeline provides us with a historical snapshot of the source data.

Let’s assume we have a data pipeline pulling data every day from an application’s database. An application’s database represents the data at the current state. For example, Let’s assume we have a user table with a zipcode column. If the user changes their zipcode the application will overwrite the existing zipcode value. This is a standard OLTP database design .

Let’s assume we discover an error in a transformation step and have to reprocess the last three months’ data using the correct logic. We do not have the point-in-time data for the past three months, since the application database will only contain the current state. But, if we had stored the extracted data in a staging area, we can run a backfill with the correct logic on the data in the staging area.

Thus we can see that the staging area stores historical snapshots of the source data. The staging area removes our dependence on the source for historical data.

Here are some more example scenarios where staging areas can be helpful.

  1. Staging data from scraping websites provides us with a historical snapshot of the website. The staging area is crucial since the website may block scrapers, rate limit calls, etc.
  2. Data discrepancies can be traced back to raw source data. This data lineage is crucial when debugging issues with your data pipeline.
  3. If the incoming data adds a new column after a specific date, we can modify our load script with a simple date-based if-else logic to account for this. Having date-based logic allows us to handle schema evolution.
  4. Backfill destination data with new transformation logic applied on staging data.

Some data pipelines have a raw/base/landing zone area, where the data extracted from the source system is stored, and a staging area, where the raw data is transformed to have consistent column names and types.

The business logic is applied to the staging data and is tested before being inserted into the final tables.

5. Conclusion

Hope this article gives you a good understanding of what a staging area is and why it’s crucial in a data pipeline. The next time you are building a data pipeline, make sure to have a staging area and your future self will thank you.

If you have any questions or comments, please leave them in the comment section below.

6. Further reading

  1. What is a data warehouse
  2. 10 skills to ace your DE interview

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