Decoding the Modern Data Stack — Reverse ETL

Making the Most out of Your Data

Source: Mark Fletcher-Brown, Unsplash

Data infrastructure has undergone huge changes in the past few years. First, we have moved from the Extract, Transform, Load (ETL) approach to ELT — where raw data is loaded into the warehouses before transforming it. I’ve talked about the two in this blog. Organizations then started adopting a new approach: reverse ETL. And in the past couple of years, reverse ETL tools have become key components of the modern data stack.‍

A brief summary of ETL and ELT

As organizations collect data from multiple sources — such as CRM systems, Cloud applications, and more — the ETL process aims to gather the data collected from separate sources and create a centralized database. Practically, ETL pipelines have the role of extracting raw data from its source, transforming it, and finally loading it in the warehouse — which is a centralized database.

Traditional ETL is characterized by the performance of the transformation process before the loading into the warehouse. This is because, back in the day when ETL was created, storage, computation, and bandwidth were very expensive. Hence the need to reduce the volume of data before it gets to the data warehouse.‍

What is reverse ETL?

To put it simply, reverse ETL is the exact inverse process of ETL. Basically, it’s the process of moving data from a warehouse into an external system — like a CRM, an advertising platform, or any other SaaS app — to make the data operational. In other words, reverse ETL allows you to make the data you have in your data warehouse available to your business teams — bridging the gap between the work of data teams and the needs of the final data consumers.

‍The challenge here is linked to the fact that more and more people are asking for data within organizations. This is why organizations today aim to engage in what is called Operational Analytics, which basically means making the data available to operational teams — like sales, marketing, etc. — for operational use cases. However, the lack of a pipeline moving data directly from the warehouse to the different business applications makes it difficult for business teams to access the cloud data warehouse and, consequently, to make the most out of the available data. The use of the data sitting in the data warehouse is limited to creating dashboards and BI reports. This is where the bridge provided by reverse ETL becomes crucial to fully use your data.‍

Enabling operational analytics with reverse ETL

Practically, having the bridge brought forward by reverse ETL is what allows companies to perform actions like crafting personalized marketing campaigns, driving product-led growth at scale, and much more. Here are a few examples of the benefits of reverse ETL in practice:

• Data teams often spend a lot more time working to meet the basic needs of other teams (e.g., custom integration work) rather than contributing to the overall company strategy. Operational Analytics allows data teams to become key organizational stakeholders by making them spend less time on mechanic integration work, and allowing them to always work with reliable data.
• To be able to convert customers and turn them into loyal clients, sales teams need data to optimize every touch point and ensure that every interaction is perfectly timed. Operational Analytics enables sales teams to understand what are the features that customers appreciate the most, it gives them access to high-quality behavioral insights, and much more.
• For marketing campaigns to work, it is essential to know who you are talking to. Operational Analytics allows marketers to segment their audience and personalize their messaging.

‍In other words, when business teams can easily operationalize their data, they can work smarter to quickly solve the issues they have at hand.

The key difference between traditional analytics and operational analytics is that instead of giving business teams pre-packaged information in the form of dashboards or reports, they can actively choose the next best action for the data they have available.

Only having one rather than multiple pipelines to manage benefits both data and GTM teams. So, with reverse ETL, data teams no longer need to write scripts and overview syncs. At the same time, the business teams can analyze and get insights from consistent and reliable data.‍

Do you need a reverse ETL tool?

There are several reasons why companies should adopt reverse ETL:

• Reverse ETL is key to breaking down data silos across teams. Data warehouses are necessary for today’s businesses — they are a safe, reliable, and affordable tool to aggregate and centralize data. However, the data warehouse can become a silo itself. This is because, without reverse ETL, core business metrics only live in the data warehouse, preventing teams from making the most out of the available data.
• With reverse ETL, the data can leave the data warehouse and get right to the center of the business. In other words, reverse ETL enables data activation, unlocking the knowledge of your data and making it actionable for the data consumers within the end tools they use every day.
• In addition, companies constantly face a considerable amount of manual requests for data and sometimes struggle to automate them. Imagine a marketing team that wants to sync a list of new users to target them through Google ads. Most likely, the organization the team is operating in already has the needed data available in the warehouse. And with Reverse ETL, the organization can extract and sync that data to external tools, making it easier to enable data automation.

‍What do you need to look for in a reverse-ETL tool?

Reverse ETL tools are not all the same. Here are some of the key factors you should look for when choosing a reverse-ETL tool:‍

Reliable syncing

Syncing is what keeps data aligned in real-time. If syncing is not working, your teams and systems will work with faulty data.‍

Security and privacy

Organizations today not only need to keep their data secure for themselves but also for the regulations that are currently being more and more implemented. Reverse ETL tools need to prioritize security and privacy.

‍Integrations

Another very important thing to remember is the tools the specific reverse-ETL tool can integrate with. This is particularly important if you already use several apps, services, and systems across the organization.

‍Conclusion

Reverse ETL tools help companies of different sizes and industries to bring data into their daily operations by sitting on top of data warehouses and handling different functions of the modern data stack.