Reverse ETL


Brands that truly value their customers dream of a seamless data stack. And until recently, achieving that dream was a pretty big challenge. However, the advent of cloud data warehouses has revolutionized how organizations handle their data by providing a centralized platform for efficiently storing and analyzing data.

Yet, as businesses seek to unlock the full potential of their data across teams, a new demand has emerged: the ability to seamlessly integrate data back into operational processes. This growing need gave rise to Reverse ETL, acting as a bridge between data warehousing and real-time operational agility.

Let’s delve into the fundamentals of this process and explore its roles within the modern tech stack of today’s data-driven marketing world.

What is Reverse ETL?

Reverse ETL is a powerful data integration process that reverses the traditional Extract, Transform, Load (ETL) flow. It involves copying customer data from your data warehouse into operational databases, applications, and SaaS platforms so your marketing, sales, and other teams can use it.

In simpler terms, Reverse ETL lets you take user data from your central storage repository and share it with your frontline business teams so they can leverage it in their favorite tools to drive action and personalize customer experiences.

This versatile approach to data management is giving businesses the power to tap into their core metrics and enhance their marketing and sales efforts. With Reverse ETL, syncing vital data points back into business processes becomes effortless.

But, to truly understand the impact of Reverse ETL (and why it’s more than just another data pipeline), let’s look at what traditional ETL pipelines brought to the table for business and data teams.

The difference between ETL and Reverse ETL

You might have come across the terms ETL and Reverse ETL in the context of data centralization. While both perform important functions in powering your martech stack, they serve very different purposes.

The traditional extract, transform, load (ETL) data pipeline process has remained largely unchanged since the 1970s. The process involves extracting the data from the source, transforming it into a usable format, and then loading it into your data warehouse or data lake.

Let’s break this down: 

ETL data movement


  • Extraction: we pull data from source systems like operational databases, applications, or external APIs.
  • Transformation: the extracted data undergoes extensive transformations, cleaning, and restructuring to fit the schema and format required by the data warehouse or analytics platform.
  • Loading: The transformed data finds its new home in a central data repository, usually a data warehouse or data lake. This storage is optimized for analytical queries and reporting.

ETL is the go-to for batch processing, making it perfect for historical analysis, reporting, and business intelligence. It shines in data consolidation, cleansing, and transformation tasks for structured reporting and analytics. ETL processes are often scheduled at specific intervals (think nightly batch processing) to refresh the data warehouse with the latest information.

Reverse ETL data movement


On the other hand, the data movement process for Reverse ETL looks like this:

  • Extraction: we flip the script and extract data from the data warehouse or data lake, where we usually store our historical and analytical data.
  • Transformation: Data may undergo minimal transformations, mainly to make it accessible and usable in operational systems.
  • Loading: The transformed data is then loaded into operational databases, SaaS applications, or other tools to use for real-time or near-real-time operations.

In a nutshell, ETL and Reverse ETL are like two sides of the same street, with traffic moving in opposite directions. ETL moves data into the warehouse and gets it ready for analysis. Reverse ETL makes it possible to take that data out of the warehouse and put it to work, delivering real-time insights and enhancing decision-making capabilities for frontline teams. 

Why you need Reverse ETL

Businesses rely on Reverse ETL to tackle crucial requirements in the modern data-driven landscape. They now have unprecedented power to unlock value from their stored data in ways that were challenging with traditional ETL methods.

Preventing data silos


For many enterprise brands, the data warehouse often becomes the final resting place for data. As a result, the very platform designed to eliminate data silos can become a data silo itself, hindering collaboration and creating inefficiencies. Since Reverse ETL moves data out of the warehouse, teams can access all data sources and datasets, regardless of their origins. This newfound freedom means marketing teams are no longer confined to the data living in their business tools.

Data activation


The value of data lies in its application. Without a way to pipe the data from the warehouse to the tools that matter for customer engagement, it’s of little value to non-technical marketing and sales teams. Reverse ETL transforms data from a passive resource into an active asset readily available for immediate use. This empowers teams to respond promptly to evolving customer needs and behavior.

Operationalize your data


The power of your customer data is fully realized when it’s operationalized and integrated into your day-to-day business processes. When frontline teams can use it in their favorite tools, you’re turning data into a concrete, measurable component of marketing, sales, and customer support activities. Businesses operationalizing their data through Reverse ETL can make data-driven decisions in real time, optimize processes, improve customer experiences, and ultimately enhance operational efficiency. 

Reverse ETL vs. CDPs

Customer data platforms (CDPs) are third-party data storage services that serve as both a mini data warehouse and data activation solution. These platforms are also created to give you identity resolution, audience management, and built-in data activation across your other tools.

 At first, it can simplify the process of activating stored data, but there are drawbacks to using a CDP:

  1. Data Privacy – CDPs store your data outside your firewall, causing concerns around GDPR and CCPA, especially when working with PII.
  2. Inflated Costs – pricing models on CDPs can get expensive, especially at the enterprise level. Storing and activating data points across a large record of customers can become cost-prohibitive.
  3. Stale Data – Transferring data from your data warehouse like Snowflake to a CDP can take hours to ingest all the data. Having your CDP deploy this data to a SaaS solution like your ESP can take even longer. This makes the agility out of your marketing and creates a severe data lag. Flash sales and other timed events become a nightmare. Say goodbye to real-time data when using a CDP.
  4. Slow To Transition – Switching to a CDP can take months, if not a full calendar year, to phase in the new tech and get your marketing team versed in the new system. A warehouse-native solution like MessageGears cuts this time down dramatically.

Perhaps the biggest downside is that you no longer own the data once you deploy a CDP. While you may still have copies in your company’s data warehouse, the CDP simultaneously hosts the data on its own server. This can cause significant concerns, particularly if your data is subject to HIPAA or other privacy regulations. Other disadvantages include the inability to completely customize your data. And on top of that, the cost of using a CDP is extremely high.

How does MessageGears solve for Reverse ETL and CDP needs?

MessageGears is a cross-channel marketing platform that connects directly to your data warehouse without needing to copy, sync, or map your data like traditional SaaS tools. With powerful Reverse ETL functionality, our audience segmentation tool is purpose-built so that non-technical marketers can easily activate customer data on any channel in real time. This includes channels within the MessageGears platform—like email, SMS, mobile push, and in-app messaging as well as third-party channels, like your social or Google Ads campaigns. You can create dynamic audiences that allow your team to send highly personalized cross-channel messaging campaigns based on anything and everything you know about your customers. See how we fit in the modern data stack.

Looking to talk with an ETL expert? Reach out, and we can help you find the right solution.

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