What is reverse ETL? A complete guide + best tools

When surveying executives and IT leaders across 12 countries, Seagate and IDC came across a surprising statistic: these enterprises were only using one-third of their data. The rest was often locked inside a data lake or warehouse, with non-technical teams having limited access and visibility. In a world obsessed with being data-driven and having cutting-edge insights, it seems egregious to have two-thirds of your data remain virtually inaccessible. After all, data can be the pathway to boosting revenue, winning back customers, and streamlining operations. You just have to know how to use it. 

To effectively leverage the data you collect, businesses need to first ensure its quality, ticking off boxes like:

• Is the data complete (e.g., consolidated)?

• Is it up to date (e.g., updated in real time)? 

• Is it accurate (e.g., no duplicate entries)? 

Breaking down data silos, consolidating data from across your tech stack, and implementing data governance at scale are step one to having data that’s ready for use. But step two involves taking the data you’ve enriched in your warehouse and making it available in the tools and systems your teams use every day – which is where reverse ETL comes in.

Table of Contents

1. What is reverse ETL?

2. What is the difference between ETL and reverse ETL?

3. Understanding the mechanics of reverse ETL

4. Why businesses need reverse ETL

5. Where reverse ETL fits into your data infrastructure

6. 3 factors to consider before implementing reverse ETL

7. 4 best reverse ETL tools

8. FAQs

What is reverse ETL?

Reverse ETL is the process of copying data that’s stored in a data warehouse and sending it to downstream tools and business applications like a CRM, marketing automation software, or analytics dashboard for activation. 

Reverse ETL helps ensure data is synchronized across all the tools and applications a business uses in its day to day – or in other words, making sure data remains consistent and up to date wherever it’s stored. Data warehouses act as a central repository for businesses, a place where data can be consolidated after its cleaned and properly formatted. This allows businesses to gain a holistic view of their operations and customer behavior, and presents an opportunity for further enrichment (e.g., understanding how your online advertising is influencing in-store conversions). 

What is the difference between ETL and reverse ETL? 

You may recognize the acronym ETL, which stands for “Extract, Transform, and Load.” ETL is the process of collecting data from various sources, cleaning and structuring it to match its target destination (i.e., transformation), and then loading it into a repository like a data warehouse. (There’s also the option to load data into a repository like a data lake before transformation takes place, in a process called “ELT.”) 

A simple way to think of the difference between ETL and reverse ETL is that they represent two sides of the street, with traffic moving in different directions. ETL is focused on moving data into the warehouse for consolidation and cleanliness purposes. Reverse ETL is concerned with taking that cleaned, enriched data out of the warehouse and moving it into downstream tools for team-wide use. 

Understanding the mechanics of reverse ETL

Now that we’ve given an overview of reverse ETL, let’s get into the mechanics of how it works. (There are several tools to help with this process, which we outline in more detail in later sections). 

1. Step One: Extraction. This involves querying your data warehouse (e.g., via SQL) to extract the specific data that you need.  

2. Step Two: Transformation. The data that you extract will be in a specific format (data warehouses typically store structured data). So, you may need to transform data so that it matches its target destination. This is where data mapping is pivotal, to trace the movement of data between storage systems and tools into specific fields. 

3. Step Three: Loading. This is when data is loaded into its target destinations. This can be done via an API integration, manual upload, batch processing, etc. (We list tools to help automate this process in later sections.) 

4. Step Four: Activation. Once data is loaded into downstream tools and applications, it can be leveraged by internal teams and even trigger specific actions (e.g., launching a personalized customer interaction based on their recent online and offline behavior). 

5. Step 5: Ongoing Monitoring. As with any process, it’s important to continuously check for quality. Many reverse ETL tools are able to automatically flag failed syncs or errors to help investigate issues. 

When we talk about reverse ETL, we tend to focus on four components: 

1. Source: This is where data originated from, which could be a website, cloud application, mobile SDK, etc. 

2. Models: This refers to SQL queries that define and specify which data sets you want to synchronize with downstream tools. 

3. Destinations: These are the tools and applications you want to deliver data to from the data warehouse. 

4. Mapping: This is when you map data from your warehouse to specific fields in your target destinations. 

Why businesses need reverse ETL

A reverse ETL tool is a powerful component of the modern data stack, and all teams and departments stand to benefit from this process. 

Operationalize your data

“Operationalization” doesn’t just mean using something. In scientific research, it means making an abstract concept concretely measurable.

In the same way, data that sits in a warehouse has a vague potential to contribute value to your business. But when you use it in business apps, you turn it into a concrete, measurable component of activities like marketing campaigns or product development.

All departments in a company can operationalize data. For example:

• Finance creates a custom payment plan for B2B customers and sends automated follow-up emails using an invoice and accounting software like Xero.

• Customer Support prioritizes the queries of customers marked as “VIP” on a ticketing system like Zendesk.

• Marketing runs a re-engagement campaign using an email tool like SendGrid whenever a person abandons a shopping cart and fails to complete checkout within 24 hours.

Prevent data silos

Reverse ETL tools let you access data across different departments. For example, sales reps are not confined to sales data. You’re limited only by the restrictions your company sets for privacy and security reasons.

As a result, data silos break down, and you no longer have to keep begging another team or a data analyst to create a list or report for you. You can load the relevant data into the app you’re using. For example:

• Product gets a list of high-value customers and gives them early access to a new feature you’re rolling out on your SaaS app. The data is finance-related but isn’t siloed within that department.

• Sales invites people who downloaded a case study to view a product demo. No need to ask the marketing team to send them details whenever someone downloads it.

• Accounting discovers that some customers with late payments also complained about your product in the last 30 days but didn’t get their issues resolved. The accounting staff works together with customer support to resolve the problems before following up again on the payment.

Easily integrate and scale your analytics

Integrate a reverse ETL tool with operational analytics software to get concrete answers to questions like:

• What is the best predictor of customer churn?

• What traits and behavioral patterns do customers with the highest lifetime value have in common?

• How does the user onboarding experience affect customer loyalty?

• What does the customer journey of a certain audience segment look like?

• Which communication channel has higher engagement?

• Does our product recommendation algorithm lead to larger basket sizes.

To answer questions like these, you need data from multiple channels and departments. And as your business grows, you want to continue asking and answering data-driven questions without having to set up new analytics workflows. Reverse ETL tools make that possible as you only need to integrate them once with your business intelligence and analytics software.

Give data teams more time to focus on higher-value work

Once you connect a reverse ETL tool to your data warehouse, you reduce the need for data analysts to manually extract and prepare data. Analysts face many requests like these every day, which means they can spend hours performing a relatively simple task over and over again. 

Implementing a reverse ETL tool saves data teams a lot of time and lets them focus on solving more complex data problems, such as maintaining a high quality of data, implementing security and privacy practices, and identifying the most useful metrics and information for your business goals and problems.

Where reverse ETL fits into your data infrastructure

Reverse ETL and data warehouses

Reverse ETL tools connect to widely used cloud data warehouses like Snowflake, Google BigQuery, and Redshift. They can also use spreadsheets as sources.

Once you’ve linked to your source, choose the app where you want to activate and sync specific data tables or data sets. If you can’t find one of your business apps on the list, send the reverse ETL tool provider a request for integration.

You take the above steps within the interface of the reverse ETL tool. It’s typically a point-and-click process that doesn’t require you to write SQL.

Reverse ETL and CDPs

The capabilities of a CDP and reverse ETL go hand in hand, helping businesses to effectively manage and leverage their data throughout the lifecycle. 

A CDP collects data from multiple sources, cleans it, unifies it to create customer profiles, and then feeds the transformed data into warehouses. The reverse ETL tool then takes data from warehouses to downstream tools and apps for activation.

The value of combining a reverse ETL tool and a CDP lies in having enriched customer profiles from the CDP. CDPs perform identity resolution, which means they can identify the same customer across different interactions. 

For example, a CDP might find that the name and email address of a new customer match those of a person who viewed a product demo months ago. Or that the Instagram user who liked your post about your anniversary sale also bought products from you during that sale. The CDP stitches these disparate pieces of information together, creating a profile of a customer’s interactions with your business over time and across multiple channels. 

3 factors to consider before implementing reverse ETL

Before going ahead with reverse ETL there are a few things to consider, from team bandwidth to data volume and then the pricing structure of tools. 

1. Data volume 

The World Economic Forum estimates that by 2025, 463 exabytes of data will be generated daily across the globe. As data volume continues to increase at an exponential rate, businesses need to consider the amount of data they’ll need to extract from their warehouse to send to downstream tools, and how regularly they’ll need to do this to ensure synchronicity. On top of that, pricing for reverse ETL tools can be tied to data volume, so it’s important to consider this as well. 

2. Data integration complexity

Implementing reverse ETL can be a complex process, for a multitude of reasons. When trying to determine just how complex this will be, consider the following: 

• Your different data sources, how data is formatted, and how you’ll make this data compatible with your reverse ETL tool and its target destinations. 

• How you’ll handle and resolve data inconsistencies to ensure quality at scale. 

• If the reverse ETL has pre-built integrations with the tools in your tech stack (to help streamline setup). . 

• How you’ll protect data throughout the extraction and transformation process (something that’s especially important if you’re in a business that deals with personally identifiable information, like healthcare). 

3. Scalability

Being able to implement reverse ETL at scale is another important factor to consider. Think of the impact that the reverse ETL process could have on your system resources, network bandwidth, and processing capabilities, especially if you’re working with large volumes of data. 

4 best reverse ETL tools

Census

Census  is user-friendly for non-technical staff like marketing and customer support teams. They charge based on the number of destinations and/or destination fields, access control features, and service-level agreement terms. 

Hightouch

Hightouch enables businesses to sync data from their warehouse with the tools and apps in their tech stack. Hightouch features several no-code and marketer-friendly  features like audience creation and activation. 

Grouparoo

Grouparoo is an open-source reverse ETL framework, and well suited  for developer-driven projects and data-mature companies as it offers a high level of control over data modeling and user segmentation.

Twilio Segment

With Twilio Segment, businesses are able to harness reverse ETL alongside all the capabilities of a complete customer data platform (i.e., real-time identity resolution, a configurable identity graph, profile portability, automated data governance at scale to ensure privacy and compliance). Businesses can then enrich their identity resolved profiles in their data warehouse before syncing them with downstream tools for activation. 

Segment’s flexibility and pre-built connectors also make it seamless to integrate into businesses’ tech stack, reducing implementation time. Learn more about Segment’s reverse ETL feature here.