If you’re a data junkie like ourselves, you’ve probably heard the buzzing in the tech sector about reverse ETL. This process is a deviation of traditional ETL, also known as “extract, transform, load.” 

So what is Reverse ETL? In the simplest terms, reverse ETL allows data teams to get data in real-time and make decisions based on it. Instead of extracting raw data from sources, transforming it, and loading it into data warehouses, reverse ETL extracts data straight from the warehouses. 

Picture this: you have a multitude of data cells on your customers within your central data warehouse. Using just that data, how would you best send an offer to those customers? You’d have to manually analyze this data, which could take an extremely long time, especially if you have a lot of customers. 

Reverse ETL is eliminating this issue for companies of all industries. Why are so many data teams utilizing this complex process nowadays? Discover the basics of this unique data integration process below! 

The Basics of Reverse ETL 

With traditional ETL, data is extracted from outside sources, transformed into a safe, compatible resource, then loaded into a third party system like a data warehouse. From there, other users can access this data and use it to make decisions. 

Yet, with reverse ETL, this process is flipped backward. First, the data is extracted straight from a data warehouse like a cloud server. Then, instead of transforming the data on a secondary processing server, reverse ETL transforms it within the warehouse it’s sourced from. 

Data warehouses include data from many different sources. These warehouses help users analyze data and reduce, if not eliminate, data silos. 

Ultimately, as these data silos are removed, the warehouse becomes a data silo itself. From this point, users may know its value, but are limited in their ability to act on its insights, since it’s not in the workflow inside the apps they use. 

This is what reverse ETL is for. Essentially, it is an ETL data pipeline, just in reverse. In reverse ETL, instead of the data warehouse being the final target, it acts as the source. Then, the insights from this source are formatted and pushed out to third party apps. 

As a result, instead of having to go to a  page or app to see insights from customers, you can see it straight from your work apps. This process allows you to push these insights to your workflow, allowing you to act on it sooner.  

How It’s Used

Now, you may be wondering how this process is actually used. Well, you are basically turning your insights into operational analytics. Reverse ETL automates everything for you, which significantly increases your productivity. 

Instead of having to manually analyze your data or load it into a system that analyzes it for you, an analysis will automatically be available in your workflow. For instance, if you have insights about 100 customers for your business, reverse ETL will provide you with trends from that data. 

There are an unlimited amount of applications for this process, but here are a few: 

  • Mark subscribers who downloaded a free e-book as leads in Salesforce
  • Make customized email campaigns based on app usage and user location
  • Provide customer app usage history to Zendesk for support brands
  • Gain access to granular customer segmentation for ads from a Snowflake data warehouse

With hard data backing every business decision your team makes, your company is sure to increase its sales and profits considerably. 

Reverse ETL vs. CDP

Now, if you’ve heard of reverse ETL, you’ve probably also heard of CDP. To truly understand reverse ETL to its core, you must also have a strong comprehension of CDP, or customer data platforms. 

CDPs are systems that collect customer insights from different sources, cleans them, and combines them into a neat, organized view. These insights become actionable when they are made available to other marketing systems. 

This is very similar to how reverse ETL pushes data points to warehouses. However, there are a few key differences between the two methods. 

For one, CDP is a system, while reverse ETL is a process. Furthermore, CDP’s scope is limited to consumer data, while reverse ETL is applicable to many more purposes. In addition, the primary users of CDPs are marketers. However, reverse ETL users are largely data specialists and power users. 

Lastly, CDP extracts information from raw customer data. On the other hand, the process of reverse ETL starts from the data warehouse.