Reverse ETL: The What, Benefits and Use Cases
ETL is a data management process that’s integral to data warehousing. It can be not only complex but also time-consuming. Reverse ETL is a new method growing in popularity because it’s simpler and can be completed faster. This article explains what is reverse ETL, how it works, and the pitfalls to avoid. It also talks about the benefits and drawbacks of reverse ETL, so one can then decide if it’s suitable for their organization.
What Is ETL And Reverse ETL?
Reverse ETL is a process of extracting data from a data warehouse, transforming it, and then loading it into a third party system. Traditional ETL, which follows the Extract-Transform-Load sequence, or even ELT, which stands for Extract-Load-Transfer, pushes data from third-party systems, such as CRM, HubSpot, Oracle, and MySQL, into target data warehouses. On the other hand, reverse ETL makes the data warehouse the source instead of the target, while third-party systems are the target. This, in effect, means data emanates from the warehouse toward the designated third-party destination. Essentially, reverse ETL is about operationalizing your data.
Reverse ETL works by first extracting clean and processed data from a data warehouse into a separate data extraction process. In this process, the data source is the data warehouse itself. The data in the source is then transformed to meet the specific requirements of the target system. Once the data has been transformed, it is loaded into the target system. One can use this method for various purposes, including data migration, synchronization, and replication.
Thus, reverse ETL involves extracting data from a data warehouse, transforming it within the warehouse to meet third-party system data format requirements, and loading the data into the third-party system for processing. This technique can keep data in sync between a data warehouse and a data lake or migrate data from one data platform to another. It is also sometimes used to perform data cleansing or data enrichment tasks.
Because data warehouses cannot load data directly into third-party systems, and the data needs to be “processed” as an in-between step, the method is thus called reverse ETL and not reverse ELT. As the data transformation is carried out within the data warehouse, it is not considered a traditional ETL process. Neither data is transformed by an “in-between” server.
What Are The Benefits Of Reverse ETL?
This method has many pros, which include:
- The potential to quickly and easily move data from one system to another.
- The capability to keep data synchronized between two systems.
- Access to accurate and timely data.
Occasionally, this technique can prove to be a challenge to implement. The most common difficulties include that data may not be processable in the target system. Further, data may not be compatible with the source system or may need to be transformed before it can be loaded into the source system.
Who Can Benefit From Reverse ETL?
Reverse ETL allows almost any department within an enterprise, including Sales and Marketing, and Finance to access the data they need whenever it wants. It is normally used when data needs to be migrated from one system to another or when data from multiple systems needs to be combined into one system. Reverse ETL can also be used for data cleansing and data enrichment.
Why Reverse ETL?
Comes with a bunch of advantages, one of them being it democratizes data analytics. Without reverse ETL, your analysis will remain locked inside your dashboard.
The reverse ETL process is the engine that drives operational analytics, continuously pushing through real-time customer information into third-party applications to ensure the right person has all the data and insights they need in their decision-making.
Use Cases Of Reverse ETL
There are many use cases for reverse ETL. One everyday use case is data migration, where data from one system has to move to another. Another common use case is data warehousing, where data from multiple destinations needs to be deposited into one repository.
Reverse ETL can also be used for data analysis to change the data from one system into a format that another system can use. For example, data might need to be normalized before the data analysis application can use it. Or it might require further processing on raw forms of JSON so developers who don’t usually work with datasets created outside their ecosystem could access them more quickly—or even at all!
In your enterprise’s modern data tech stack, reverse ETL can be done in three different ways. Here’s how:
To move data from a warehouse to a business app, your tech team can write individual connectors or use APIs. But often, this is not possible for small companies, as they cannot afford to have very large groups.
But you can use the native integrations that connect tools. This has an inherent drawback: it can be a winning approach with one particular tool, but then again, not every SaaS tool may have the native integrations your business needs.
Lastly, your business can use purpose-built reverse ETL solutions available in the market. These come preloaded with the connectors you need to ingest data from applications into your data warehouse. Using the Reverse ETL solution, you can quickly load data from the warehouse back into business applications without any complicated setup.
Here’s a use-case: If a MicroStrategy customer lifetime value (LTV) score is not processable in Salesforce, your data engineer can apply an SQL-based transformation to this report data within Snowflake to isolate the LTV score and format it for Salesforce. He can then push it into a Salesforce field, allowing marketing and sales teams to use that information.
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