In today’s economy, data has become increasingly important. As technological capabilities advance, more and more businesses are beginning to use data to track and optimize their performance.
One of the main processes used to source and organize data is ETL, or “extract, transform, load.” Within this process, users are able to extract data from different sources, transform it into a functional, safe resource, then load that data into systems that other users can access.
Essentially, ETL is used to centralize data in a data warehouse. However, it is also used to send data from data warehouses to third party systems. This process is known as “Reverse ETL” and is quickly becoming an industry standard among tech companies.
If you’re looking to set up your data team for success, you’ll need to educate yourself on a few key points. To learn more about reverse ETL and how it works, keep reading below!
ETL
To understand reverse ETL, you need to first gain a firm grasp on traditional ETL. This process fits under the category of data integration methodology.
Through ETL, raw data is extracted, transformed on an alternate processing server, then loaded into a target database. This database can be loaded with a myriad of different data cells and organized according to the user’s preferences.
ELT
Recently, as more companies have moved to cloud data warehouses, ELT (extract, load, transform) is starting to replace ETL. Unlike the ETL process, users do not have to transform their data before loading it.
Instead, they can load the raw data directly into a cloud data warehouse. From there, users can conduct data transformations from inside the warehouse through SQL pushdowns, Python scripts, and other code.
ETL and ELT both move data from third party systems to target data warehouses. The third party systems used are typically business applications like Hubspot and Salesforce, while the databases are usually cloud applications like Oracle and MySQL.
The Reverse ETL Process
With reverse ETL, the source is the data warehouse, not the target. The target acts as a third party system. Data is sourced from the warehouse, transformed within that warehouse to meet data formatting requirements of the target, then loaded into the target for action taking.
This method is known as reverse ETL, rather than reverse ELT, since data warehouses are unable to load data straight into third party systems. However, this process deviates from the traditional ETL process because data transformation is done within the warehouse. As of right now, there is no intermediate processing server that can transform the data.
To illustrate how this process would be used in the real world, consider this: if you have a customer lifetime value score in a Tableau report, you can’t process it in another app like Salesforce. This is because the data requirements of these apps are different.
In order to transfer the data, a data engineer would first have to apply an SQL-based transformation to the data inside of Snowflake to isolate the LTV score, format it for Salesforce, then move it to a Salesforce field.
Impact Of Reverse ETL
This process allows businesses to operationalize their data by pushing data back into third party systems like business applications.
Any team can benefit from using reverse ETL, whether you are in sales, marketing, or product. There are an innumerable amount of applications for reverse ETL.
Some popular examples include syncing internal support channels with Zendesk to enhance customer service, moving customer data to Salesforce to optimize sales, adding product analytics to Pendo to improve customer experience, and organizing support, sales, and product data to customize marketing campaigns for customers.
Even if your company already uses a cloud data warehouse, you can benefit from reverse ETL. Sometimes, data ends up in the wrong hands. Reverse ETL solves this issue by transferring data directly into applications leveraged by line-of-business (LOB) users.
Some teams already have access to data via BI reports but unfortunately, these reports are often underused. The data that really is impactful for teams are the systems and processes that they already know.
This is where reverse ETL comes in. Reverse ETL allows businesses to utilize data in an operational capacity. In real-time, teams can make decisions based on this data.
Reverse ETL can also help streamline data automation by eliminating manual data processes like CSV pulls and imports. These processes can be time-consuming and slow down your workflow.
You can use reverse ETL to complete a step in a broader data workflow. For example, if you’re creating an AI/ML workflow on top of your Databricks stack, you can use reverse ETL to move formatted data into the sequence.
