Reverse ETL Fundamentals: A Comparative Guide to Activating Your Data Pipeline

Reverse ETL. Data activation. Operationalizing records. These are all terms for a process that's become increasingly essential for data-driven organizations: getting information from your database and into the hands of various teams and people in your company.

While ETL is a process designed to help move data into a database for analysis and organization, reverse ETL helps move data out to use in daily operations. And just like its forward-thinking counterpart, reverse ETL comes with its challenges.

This guide will briefly touch on the basics of reverse ETL before comparing it with other data pipeline processes to help you optimize your data stack for the best results.

Key takeaways

• Reverse ETL is a process that enables data to flow from data warehouses and other data stores into business dashboards and tools, allowing companies to operationalize data in daily activities.
• ETL, ELT, and reverse ETL are different ways of processing data. Sometimes you'll need to use more than one, depending on your business goals.
• CDPs can use your reverse ETL pipeline to deliver data to downstream destinations and allow for more personalized marketing campaigns.
• Reverse ETL is a more straightforward and cost-effective solution than iPaaS and open-source frameworks. It’s also compatible with most data warehouses and operational systems.

What is reverse ETL?

Reverse ETL is a data management process in which the Extract, Transform, and Load (ETL) cycle is reversed. Rather than loading data into a data warehouse from external sources, reverse ETL flips the flow by extracting data from the data warehouse or mart and transforming it into a format suitable for external systems.

A diagram showing the reverse ETL data flow from the data warehouse to operational systems.

ETL, ELT, and reverse ETL are merely different sequences and combinations of extract, transform, and load. It's just a matter of which system is the source and which is the target.

The practical applications of reverse ETL are endless, but here are a few examples:

• Convert leads data from Salesforce into a format easily imported into Iterable for further marketing automation processes.
• Update master customer profiles in your CRM by combining data from multiple sources (e.g., orders placed on your website, support tickets opened in Zendesk, and subscription activations in Salesforce).
• Convert transaction data into machine learning training sets. For example, you could use reverse ETL to convert purchase history data into training records for a custom deep-learning model that predicts customer lifetime value.

Reverse ETL can populate operational systems with data from any data store, including data warehouses, data lakes, and NoSQL databases—so get creative with your pipelines.

Reverse ETL: paving the way for operational analytics

Data warehouses are designed with analytics in mind. They're meant to store and organize large amounts of data to be queried and analyzed to uncover trends, make predictions, and inform business decisions.

Operational systems are designed for operations. They're the applications and processes that keep businesses running—such as CRMs, ERPs, and supply chain management systems.

Historically, these two systems have been siloed—for a good reason. Data warehouses are slow (by design), and operational systems are fast (also by design). But with more demand for operational analytics, data is increasingly being used to inform and optimize day-to-day operations in real-time. And that's where reverse ETL comes in.

Reverse ETL enables data to flow from data warehouses and other data stores into operational systems so businesses can use it to improve and automate their processes. In other words, reverse ETL is the glue that ties together data-driven operations, or what many call the last mile in the data pyramid.

A diagram of the Data Hierarchy of Needs.

A data pyramid has data warehouses at the bottom and operational systems at the top, with reverse ETL connecting the two. Getting data to your business units represents the final stage of effective data management, putting it to use.

Consider Trek Travel, a global adventure travel leader. After Oracle sunsetted its Bronto Marketing Platform, Trek Travel needed a way to load data from their warehouse, PostgresQL, into Iterable, our recommended cross-channel marketing automation platform. They also needed to monitor and control the data pipeline.

RudderStack made it easy to set up the necessary integrations and get the data flowing between the tools. By implementing process, we improved customer engagement for Trek Travel. The data pipeline is more robust and efficient, and we can deliver targeted marketing campaigns that meet the needs of Trek Travel's customers.

The benefits of reverse ETL extend beyond data activation. With fewer requests for reports and spreadsheets across departments, data engineers have more time to work on strategic initiatives like data lake migrations and warehouse optimizations.

And since operational systems are generally better equipped to handle real-time processing, reverse ETL can also facilitate near-real-time analytics. This is especially valuable in retail and e-commerce, where decisions need to be made instantaneously to capitalize on opportunities and avoid potential pitfalls.

Comparing reverse ETL with other data pipeline solutions

There are many ways to move around your pipeline, which can be confusing and costly, depending on your setup. However, finding what might work best for your organization is much easier if you directly compare reverse ETL with other data flows.

Reverse ETL vs. ETL vs. ELT

ETL tools extract data from an external source, transform it into the desired format and then load it into the data warehouse. ELT tools work in reverse order, loading data first and then transforming it.

Reverse ETL solutions extract data from the data warehouse (or target system) and load it into an operational system.

You'll likely need some combination of all three processes to get your data where it needs to go—which is why most data pipeline tools on the market support all three. Each method has its own set of use cases and trade-offs.

Summary

• ETL is the traditional data pipeline approach and is best suited for companies with a lot of data that need to perform complex transformations.
• ELT has become a more prevalent option because it can handle data faster due to its parallel processing.
• Reverse ETL is best suited for organizations that need to move data in real time from a data warehouse to an operational system.

Reverse ETL vs. CDP

A customer data platform (CDP) is a data management framework designed to collect, unify, and activate customer data from multiple sources. CDPs create a single customer view, which you can use for segmentation, personalization, and omnichannel marketing.

CDPs are designed to collect data, use it to create the customer profile, and then move it into an operational system.

A CDP is the best option for creating a single customer view. If your goal is to operationalize data beyond customer records, reverse ETL is the best option. However, there are benefits to using both CDP and reverse ETL tools to create a complete data management solution.

For example, with RudderStack handling your CDP and reverse ETL needs, you can use customer data from your data warehouse to facilitate targeted marketing campaigns.

Summary

• CDPs create a single customer view.
• Reverse ETL moves data in real time from a data warehouse to an operational system.
• Both CDPs and reverse ETL tools have their place in a complete data management solution.

Reverse ETL vs. iPaaS

Integration Platform as a Service (iPaaS) is a cloud-based platform that provides a complete set of tools for building and managing data pipelines. iPaaS tools like MuleSoft, Dell Boomi, and Zapier support ETL, ELT, and reverse ETL and are often used in conjunction with traditional data pipeline systems.

iPaaS is a good option for moving data between on-premise systems and cloud-based applications. However, iPaaS can be complex and expensive, so there are better options for small or mid-sized businesses.

iPaaS is an overkill if you only need to move data from a data warehouse to an operational system—in which case, reverse ETL is the better option.

Summary

• iPaaS can help you move data between on-premises systems and cloud-based applications.
• iPaaS can be more complex and expensive to manage when compared to reverse ETL solutions.
• For small or mid-sized businesses, reverse ETL is a better option.

Reverse ETL vs. open-source data pipeline solutions

Several open-source data pipeline solutions are available, including Apache NiFi, Apache Kafka, and Amazon Kinesis. You can use these tools to build data pipelines for all three ETL processes.

Open-source solutions are a good option if you're looking for a flexible and cost-effective way to build data pipelines. However, they can be complex to set up and manage, so there are better options for small or mid-sized businesses.

For example, Apache NiFi is a rather complex tool that requires a lot of expertise to set up and manage. You need to have a good understanding of Java and the Hadoop ecosystem. If you don't have the knowledge or resources to set up and manage a tool like NiFi, a fully-managed reverse ETL solution like RudderStack will be much easier to implement and manage.

Summary

• Open-source platforms could be a good option if you're looking for a flexible and cost-effective way to build data pipelines.
• Open-source data pipeline solutions can be complex to set up and manage.
• If you don't have the technical resources to integrate a tool like NiFi, a reverse ETL system is a better option.

Challenges with reverse ETL

Reverse ETL isn't the ultimate solution for all marketing and sales data problems. While it can be extremely helpful in managing data, like any data pipeline, it has limitations.

The central proposition of reverse ETL tools is to provide low/no-code solutions for non-technical users. However, these tools require technical expertise to set up and manage. Say you want to create customer journeys or do complex segmentation—you'll likely need someone who knows how to code.

And even if you have a high-performing reverse ETL tool, it's only as good as the provider you're syncing to. Each provider has different constraints, such as the number of API calls you can make daily or how the data should be transformed to fit the provider's data model.

Additionally, many pipelines rely on batch processing, which can cause delays in data delivery. The intervals range from minutes to hours, which can impact real-time decisions.

Lastly, while reverse ETL can save you time and money, it's not free. You'll need to pay for the tool and any support or training.

To overcome these challenges, choosing the right reverse ETL solution and clearly understanding your data pipeline needs is critical to getting results.

Reverse ETL and CDP: Like peanut butter and jelly

At McGaw.io, we live and breathe MarTech stacks, and we're big fans of the one-two punch in reverse ETL and CDP that RudderStack offers. We've seen firsthand how powerful this combination can be in managing customer data and driving results while keeping your data teams and business units happy.

Yes, it's not all rainbows and butterflies. No data pipeline is perfect (although the industry has come a long way). But when you get it right, the benefits of reverse ETL and CDP are undeniable.