What Is Reverse ETL and How Does It Enhance the Modern Data Stack?

April 6, 2022

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Businesses need a way to unlock the data and insights that are trapped inside their data warehouses — and dashboards — before its usefulness expires. Reverse ETL (Extract, Transform, Load) allows organizations to seamlessly move data definitions (e.g., Time to Value and Product Qualified Lead) out of storage so they are more useful in the daily work of frontline teams.

Read on to learn more about reverse ETL and how it helps eliminate data silos that can emerge in the modern data stack.

What is reverse ETL? #

Reverse ETL is the process of syncing data from a data warehouse, data lake, or data lakehouse into third-party systems — think applications like Salesforce, Helpdesk, Marketo, and Adobe Analytics. This technique has been gaining popularity in recent years as it allows businesses to operationalize information from data storage so all teams are able to utilize real-time data within their tools of choice.

For example, an e-commerce business could use reverse ETL to offer rapidly adapting personalized experiences for customers using data about their recent purchase behavior, resulting in improved satisfaction scores and lifetime value.

The excitement about reverse ETL can be traced back to an early 2021 article from Astasia Myers, Founding Enterprise Partner at Quiet Capital. In her article, Myers explains that reverse ETL was born out of a need for more effective and reliable data operationalization.

The alternative is for teams to write their own API connectors to pipe data into SaaS tools like HubSpot and Salesforce. This is often problematic because creating these connectors is a time-consuming and delicate process, as most APIs aren’t built to handle real-time data transfers and can break easily.

Reverse ETL tools #

In just the past year and a half, a number of reverse ETL tools and companies have emerged to productize this promising data trend. The biggest players in reverse ETL thus far have been Census and Hightouch, two Bay Area startups that have raised significant capital and are having a lively public dialogue about how to best benchmark reverse ETL and measure the value it provides to customers.

Other companies in this space include Polytomic, Rudderstack, Workato, and Grouparoo (the main open-source contender in this category). It will be very interesting to see how all of these companies evolve and continue to refine their contributions to the modern data ecosystem.

What’s next for reverse ETL tools #

Reverse ETL tools fall under the category of technologies that are solving the “last mile” problem in data analytics — the challenge of not only extracting insights from data but using those insights to inform actions that drive tangible results.

It’s possible that reverse ETL tools such as those mentioned above will remain in their own category and continue helping businesses use data in daily operations. However, it’s also possible that reverse ETL could become bundled with other data products as many customers crave less complexity when building their data stacks. We have already seen this with Hevo and their decision to introduce reverse ETL alongside their data ingestion solution.

Reverse ETL use cases #

Operational analytics via reverse ETL can power many potential use cases. For the sake of this article, let’s consider how reverse ETL might be leveraged by go-to-market (GTM) teams such as sales, marketing, and customer success at a product-led growth (PLG) SaaS company. PLG is an increasingly popular GTM strategy where a company uses the product itself to drive user acquisition, activation, conversion, and retention (e.g., self-service demos and trials).

Reverse ETL for sales #

Determining which accounts to focus on is an ongoing challenge for sales reps. It can be especially difficult for PLG companies where salespeople have a wealth of customer information at their disposal and may be expected to navigate multiple tools or dashboards to gather product usage data.

With reverse ETL, a PLG sales team would be able to sync relevant data into a CRM such as Salesforce to automatically identify leads who exhibit a high level of engagement with their product trial. This is exactly what a design workspace company Zeplin did when they realized their sales team severely lacked visibility into how prospects interact with their software. In addition to making product data much easier to use, reverse ETL allowed the Zeplin sales team to increase productivity as well as their close rate.

Reverse ETL for marketing #

Marketing teams face similar challenges to sales teams at PLG companies because most user data is generated in-product rather than via form fills, emails, phone calls, etc.

When no-code app development platform Retool was having difficulty getting product data into HubSpot for marketing personalization, they used reverse ETL to sync usage data (such as the types of databases a user has connected to) with their software. They have since increased their email response rate by over 32%, as they are now able to create more personalized email campaigns about the specific problems users are trying to solve.

Reverse ETL for customer success #

Customer success teams proactively identify issues and recognize the unique needs of each and every user. These frontline employees represent a company’s brand and product every time they interact with customers, which means being able to provide proper attention and care at the right time is critical. Reverse ETL makes it possible for customer success teams to sync data from various support channels so agents can pinpoint the highest priority queries at any given time.

Prior to adopting reverse ETL, the customer success team at video messaging pioneer Loom was overwhelmed by the thousands of support tickets they received every day. The high volume made them unsure which requests were most pressing or originated from high-potential accounts.

Rather than having their data engineering manager stop and write SQL every time someone wanted a complete picture of a user’s product history, reverse ETL allowed them to send data from Snowflake directly into Zendesk and Intercom. Doing so allowed customer success agents to decrease reliance on the data team and more effectively respond to increased ticket volume on their own.

Reverse ETL and the modern data stack #

It’s likely that reverse ETL adoption will only become more prevalent in the coming years as more businesses recognize its powerful potential for closing the loop between data analysis and action. Reverse ETL is especially useful for user-centric companies where business teams need help making sense of vast amounts of customer data to drive stronger decision-making and performance.

As mentioned previously, we may also see reverse ETL bundled with data ingestion products due to the similarity between the capabilities of piping data in and out. Whether reverse ETL remains its own category or merges with another, it’s clear that it presents a much more effective way to operationalize data compared to building and integrating custom connectors across the evolving modern data stack.

ETL vs reverse ETL vs ELT #

To refresh your memory about traditional ETL, ELT, and how they differ from reverse ETL, take a look at the following definitions and comparison chart:

ETL is the traditional data pipeline used to bring data from multiple sources into a centralized database, typically a data warehouse.
Reverse ETL puts ETL’s order of operations in the opposite direction, as data is copied from the data warehouse, transformed so it is compatible with the target destination’s API, and then loaded into the target application.

ELT, on the other hand, entails raw data being copied from the source system, loaded into a data warehouse, lake, or lakehouse and then transformed (check out this blog for more info about how ELT compares to ETL).

	ETL	ELT	Reverse ETL
Order of Operations	Data is extracted, transformed in the staging area, and then loaded into the target data system.	Data is extracted, loaded directly into the target system, and transformed within the system.	Data is extracted from the source system (target in traditional ETL), transformed, and then loaded into a third-party system.
Primary Focus	Loading into data systems (typically data warehouses) where compute is a valuable resource.	Loading into flexible data systems (data warehouses, lakes, or lakehouses), mapping schemas directly.	Loading into third-party systems (SaaS applications or platforms), enabling real-time connectivity.
Analytics Flexibility	Use cases and reporting models must be defined at the beginning of the process.	Data can be selected at any time for transformation and analysis as new use cases emerge.	Real-time data is consistently made available to various teams, powering operational analytics.

Innovation that improves the modern data stack #

All indicators suggest that reverse ETL is not a fleeting data trend that’s here today, gone tomorrow. In a relatively short amount of time, reverse ETL has established itself as an innovative technology that provides measurable value across different departments while also contributing to a modern data culture of collaboration and democratization.

Looking to maximize the value of your organization’s data? Learn more about the future of the modern data stack

ETL vs. ELT: Exploring definitions, origins, strengths, and weaknesses
Top 5 ETL tools to consider in 2023
Open source ETL tools: 7 popular tools to consider in 2023
Data transformation: Definition, processes, and use cases

Photo by Oleg Magni