More and more businesses are opting to use data lakes or, more likely, data warehouses these days, which allow them to store, analyze, and utilize their data from one convenient destination. But beyond creating reports and in-depth analytics, how can you truly operationalize your data warehouse into an even more vital part of your business's digital stack? Reverse ETL could provide some opportunities to do just that.

Table of Contents

  1. What is ETL
  2. Using Data from Data Warehouses
  3. Reverse ETL Use Cases
  4. Challenges with Reverse ETL
  5. In Conclusion: for ETL

What is ETL?

ETL is one method of integrating your business data from a variety of sources. ETL stands for Extract, Transform, Load and refers to linking to data sources and extracting the relevant data, transforming it into a useable and consistent format, then loading it into your desired destination. Many businesses opt for cloud-based data warehouse solutions like Amazon Redshift or Snowflake, but these aren’t the only third-party vendors helping businesses manage their data.

Rather than coding and engineering dozens of complex data pipelines, the connections that link data sources to their destination, successful businesses often choose to invest in a third-party ETL solution like That’s because there are already dozens of existing connections, meaning there’s no manual coding and no need to hire costly data engineers.

Using Data from Data Warehouses

There are many uses for carefully curated data, in fact, the more data sources you can connect to, the more useful (in theory) your data can be. CloudFactory uses’s RestAPI to connect to data sources that are considered hard to reach. That additional data allows the company to analyze performance data in depth, leading to richer insights and enhanced productivity.

As well as using analytics suites and other business intelligence (BI) tools to comb data for relevant insights, your data can be useful in other ways. Imagine if you were able to take your stored data, and send it back out to different data sources via your data pipelines. That’s the definition of reverse ETL, potentially creating a way to move data around your whole network.

Reverse ETL Use Cases

Of course, it’s fairly operational to use your business data to create complex reports or projections. But reverse ETL could, potentially, build on this further. For example, if your company uses a variety of SaaS applications, you could potentially load business-critical data into all these apps from your data warehouse so that it was accessible by everyone in the company. Or, you might want to collate customer data from a variety of sources, then upload that data directly into Salesforce for a cohesive customer profile. For companies that use a customer relationship management system (CRM), you could upload data from dozens of sources to help you answer your customers’ needs better — or even identify problems before they occur.

The key to making this data operational is to be able to use it in all the systems it would be useful, without having to manually input it yourself.

Challenges with Reverse ETL

It’s generally more difficult to insert data into a system than it is to extract it. When you’re moving data into a data warehouse using ETL, it’s already been transformed into one consistent format. When you move data back out using bespoke data pipelines to shift it into other systems, it’s possible to run across issues including but not limited to:

  • Changing data schema
  • Data conflict issues
  • API configurations
  • Compliance requirements
  • Audit requirements

Reverse ETL providers often include reconciliations steps within their process. This is a part of the process where an additional step is inserted to ensure the data is compatible with the receiving system. This step is different for every connected source, making reverse ETL a convoluted process that has to be managed carefully, whilst maintaining data privacy and security.

In Conclusion: for ETL

Many cloud-based vendors are coming up with innovative solutions to allow them to provide some reverse ETL functionality. Some offer open-source solutions for companies that want to control every aspect of the data reintegration process or hybrid architectures that allow the data to be transformed within the clients’ networks while the vendor provides just the pipelines.

The absolutely vital key to reverse ETL being effective is having accurate and complete data in your data warehouse, and that means ensuring you have an effective data integration solution like ETL. provides the most advanced securETL platform, giving businesses and data managers all the tools they need to create all the data pipelines required for complete data integration. With the ability to schedule jobs, monitor the progress and success of jobs, and check the accuracy of data, it’s the simple way to pull your vital business data from over 100 SaaS applications and data stores.

Why not speak to us about a 14-day demo and find out how can support you?