As the name suggests, Reverse ETL flips around the order of operations within the traditional ETL process. It can be defined as the process of moving data from a data warehouse into third-party systems to make data operational. It first extracts the data from a data warehouse or data lake, transforms it as necessary, and then loads it into a third-party SaaS application or platform.
Reverse ETL enables companies to move transformed data from their cloud warehouse out into operational business tools, doing the reverse of ETL (which enables companies to ingest data into their data warehouse for transformation and modeling). It’s a new approach to making data actionable and solving the “last mile” problem in analytics by empowering business teams to access—and act on—transformed data directly in the SaaS tools they already use every day.
By democratizing access to data in this way, Reverse ETL is powering a new paradigm known as operational analytics —the practice of feeding insights from data teams to business teams in their usual workflow so they can make more data-informed decisions. Reverse ETL “operationalizes” the same data that powers reports in a BI tool by making it accessible and actionable in downstream SaaS tools. Reverse ETL is necessary because your data warehouse — the platform you bought to eliminate data silos — has ironically become a data silo. Without reverse ETL, your business’s core definitions only live in the warehouse. Companies are building key definitions in SQL on top of the data warehouse, such as lifetime value, Product Qualified Lead (PQL) and Marketing Qualified Lead (MQL), propensity score, customer health, ARR/MRR, funnel stages, etc. These insights are much more powerful if they drive the everyday operations of your teams across sales, marketing, finance, etc. in the tools they live in. Reverse ETL adopters like fintech startup Blend and internal tools solution Retool are using this new approach to pipe transformed data from their cloud warehouses into their CRMs, marketing automation tools, advertising platforms, customer support and ticketing systems, and, of course, Slack. This makes the vast amounts of customer data being collected and stored in warehouses more accessible to business teams, ultimately empowering more personalized customer experiences and data-driven decision making. Reverse ETL tools can also turn your warehouse into a CDP , enabling more flexibility and ownership of your data than a traditional off-the-shelf CDP.
Reverse ETL comes in at the “last mile”, after data has been collected and stored in your warehouse through an ETL process. Then, data is often modeled with a transformation tool like dbt. Then, Reverse ETL solutions send that data back to the tools that your business relies on (such as CRMs, Ad tools, email tools and more).
Reverse ETL pipelines can be custom-built, but like many data engineering challenges, they require significant resources to design, build, and maintain. Teams without data engineers to spare are using Reverse ETL tools which make it possible for business teams to design and build pipelines using only SQL—no third-party APIs or custom scripts required. Beyond just the transfer of data, there are also crucial features that top Reverse ETL tools provide that are difficult to build in house:
Here are some amazing companies in the Reverse ETL Tools.