Reverse ETL: A New Way to Operationalizing Data

Typically, you hear something about how you need to have clean data to make better decisions. This is always going to be a very true statement. However, for data to be truly effective, it needs to be operationalized. What does this mean? It means that data needs to be made available to the people who need it, in the format they need it, and at the time they need it.

Traditionally, data has been operationalized through ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform). ETL is a process of extracting data from a source system, transforming it into a format that can be used by a target system, and loading it into the target system. Often this is done by extracting data, doing the business logic transformations, and loading it into a data warehouse. ELT is similar in the way it works, except the concept is to continually load data from source systems and then process that on some type of schedule. There are many ways to extract data and give it value, but from here, we'll continue with the concept of ETL. Though still very valuable, ETL can be time-consuming and expensive to implement. It can also be difficult to keep up with the ever-changing data landscape. So how do we try and tackle these issues?

?Bring in the concept of reverse ETL. It is a newer approach to operationalizing data. Reverse ETL is the process of copying data from a data warehouse or data lake back into operational systems. This allows businesses to make use of the data that is already stored, processed, and mostly structured, in their data warehouse or data lake, without having to extract it again.

There are certain business cases for this to work and have it work very well. Think of a company that has a traditional Enterprise Resource Planning (ERP) system. All their data flows through the ERP and into other systems, say a Customer Relationship Management (CRM) system for example. Typically, the ETL is done for a data warehouse and then ETL is done for the CRM, which is processing whatever data it needs to be operational. So now we're doing ETL twice in this example, but you can think of how many different systems companies have in place to conduct their everyday business.

Reverse ETL has several advantages over traditional ETL in the right setting. It is more efficient, as it does not require the data to be extracted from the source system multiple times. It is also more flexible, as it allows businesses to update their operational systems with clean data from their data warehouse or data lake in or near real-time. Although this sounds great, in reality there could be a number of challenges here. Some of those challenges are centered around data governance and the quality of data being fed back into your operational systems. It is important to have a data governance program in place to ensure that the data that is being copied into the operational system is compliant with all applicable regulations. If the data is not of high quality, it can cause problems in the operational system, such as wrong names, wrong identifiers, and so on. If there's a lack of focus on data governance, then reverse ETL may pose more challenges than it's worth.

Reverse ETL is a powerful concept that can help businesses to operationalize their data. It is more efficient, flexible, and easier to implement than using a traditional ETL process that loads into multiple systems. This doesn't mean to ETL goes away entirely, but it can be much easier to manage in the long run. However, there are still some challenges that businesses need to be aware of before using reverse ETL. If you are considering using reverse ETL, it is important to weigh the benefits and challenges carefully.

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