Meltano > Resources > Data and Analytics > Succeeding with Reverse ETL
Dark Mode
Succeeding with Reverse ETL
Data and Analytics

Succeeding with Reverse ETL

The recent transition from the traditional extract, transform, load (ETL) pipeline to the modern extract, load, transform (ELT) pipeline has greatly impacted the way organizations operate. In an ETL pipeline, you can directly upload your data to data warehouses, omitting the need for data staging and mass transformations. It also enables a direct bridge to any type of data, structured or unstructured, by being integrable with data lakes.

But the innovation doesn’t stop there. Teams have expanded on ETL and ELT pipelines, to create a process called Reverse ETL. Its purpose is to make data actionable by transferring data from warehouses to third-party systems like software-as-a-service (SaaS) applications, which was previously very challenging.

In this article, you’ll be learning about the functions of Reverse ETL, its advantages, the tools that support it, and tips for its successful implementation.

What Is ETL?

To better understand when you should use Reverse ETL, you need to first look at what ETL is:

ETL is a data pipeline that ensures that data is transformed into compatible formats before being loaded into data warehouses. The ETL process was created in the 1970s, which saw a growing number of databases and a rising need for a system that could load, compute, and analyze the data. ETL remains a popular pipeline to this day, because relational databases are used in traditional systems for data storing purposes and require compatible data formats.

A typical ETL process includes extracting from data sources, staging the data in a temporary location, transforming the data to match the compatibility specifications, and storing the transformed data in a warehouse. The pipeline is beneficial because it provides quick analysis and high data compliance because of the pre-transformed data and high compatibility with a large ecosystem of traditional technologies and systems.

What Is Reverse ETL?

With traditional methods, it’s never been easy to create a seamless path for data transfers between data warehouses and SaaS tools because data transfers require the creation of custom API connectors that are fragile and inefficient with high loads of data.

Reverse ETL solves this problem by offering zero-maintenance connectors that require no build effort and can be plugged into as many SaaS endpoints as needed. Reverse ETL can be considered as the inverse of ETL/ELT pipelines. Essentially, unlike in ETL/ELT data pipelines, the direction of data is no longer unidirectional.

The key pillar of Reverse ETL is operational analytics, a method of mining insights from single source of truth (SSOT) data in data warehouses and sending them downstream to a host of third-party systems, including SaaS tools. Below are some other use cases that highlight the importance of Reverse ETL:

Enriching SaaS Data with a Global Customer View

Usual practice has been to leverage customer data as analytical workloads for making important and informed business decisions. While the process has been highly effective, it overlooks low-level and real-time touchpoints involved in daily customer interactions.

Reverse ETL offers insightful customer data that is directed to touchpoints for real-time decision-making. It also improves customer experience through personalization by sending specific details about the customer that are relevant to a particular interaction. For example, if a sales team is using Salesforce regularly for prospect and client interactions, having insights based on a recent set of phone calls or emails can significantly speed up the process and show that the organization actually cares about the individual’s preferences.

Being able to access real-time data across SaaS systems also offers a consistent global view of the customer and can be leveraged by various teams in your organization like consulting, customer support, and sales.

Using Insights from an SSOT in SaaS Applications

With ETL and ELT pipelines, it’s possible for industries to leverage a SSOT stored in data warehouses. Streaming data from this source for operational analytics means the results will be consistent across multiple touchpoints. This means you can customize the experience of thousands of customers based on insights from a common source.

For example, when data is recorded through one SaaS source, say HubSpot, and sent to the warehouse with a Reverse ETL pipeline, the data is also reflected in other SaaS touchpoints, like Notion and Anaplan.

Continuously Syncing with and Monitor SaaS Endpoints

Reverse ETL acts like a network of customer data and continuously syncs the latest data from touchpoints. It also monitors the status of the connectors and the SaaS data, and if something goes wrong, the system can immediately notify the required teams.

Users can define the conditions for system alerts and also the cases when a connection will be automatically triggered between the data warehouse and the third-party tool.

Reverse ETL Tools

If you want to implement Reverse ETL in your own organization, here are some tools that can help.

Meltano and Singer

Meltano uses the Singer framework to manage an ecosystem with hundreds of connectors, and supports 293 different data sources. It also supports plug-ins for best-in-class testing functionalities for high data quality and enables reliable data replication.

Other supported features include incremental syncing and schema validation. Meltano also makes it extremely easy to schedule pipelines that can be orchestrated through Apache Airflow.

Census

Census works on top of the existing warehouse and only requires a one-time definition of the models in a data build tool (dbt). With Census, teams can create granular user segments that can highly personalize their email communications.

Some of the key features of Census include support for visual data mappers, continuous syncing, automatic connection recovery, and detailed logging.

Hightouch

Hightouch supports integrations without the need for any build effort. Without using a script, the user just has to use SQL to establish connectors to SaaS tools.

It also allows mapping management, logging, scheduling, custom API triggers, and diffed results between syncs.

Polytomic

Polytomic offers a point-and-click interface for streaming specific field data into the tool of choice and supports real-time syncing. It offers a collaborative space where users can invite other team members to create their own syncs in addition to on-premise deployment through Docker containers. It’s also GDPR compliant.

ETL vs. Reverse ETL

ETLs and Reverse ETLs don’t actually compete with each other. You can think of them more like collaborative technologies as Reverse ETLs pick up where ETLs end: at the data warehouse.

Reverse ETL overview, diagram courtesy of Samadrita Ghosh

Choosing between the two therefore depends on your organization’s current and wished-for infrastructure. If your organization has a traditional infrastructure where customer data is used for key business decisions as a matter of course, then ETL pipelines would be the ideal fit.

However, if you have a more modern technology stack or aspire to have one that integrates with multiple customer-facing SaaS tools like Salesforce, Asana, Freshdesk, or Netsuite, then Reverse ETL would definitely be the way to go for regular actionable insights to inform day-to-day decisions.

The Case for ETLs

Both ETL and ELT pipelines are built on a unidirectional data stream from data sources to data warehouses. ETL pipelines make it possible to create a common and unique store for data or an SSOT in a data warehouse that can easily integrate with data lakes. Because of its flexibility, organizations can eliminate complex infrastructures and create custom data models through simple SQL. ELTs also enable data replication without needing any third-party tools.

If an organization is looking to specifically implement an SSOT, or looking to simplify their data infrastructure for analytical workloads, both ETLs and ELTs could be a good fit.

The Case for Reverse ETLs

Unlike an ETL pipeline where the data warehouse is the target, a Reverse ETL pipeline is aimed at a host of third-party applications and SaaS tools.

While ETLs analyze data over time to support key business decisions, Reverse ETLs are built for actionable insights. Reverse ETL is also referred to as operational analytics and is the holy grail for automating the personalization of customer experience.

Organizations that are looking to leverage their data heap for daily actionable insights to use directly in customer-facing and backend operations should ideally explore the Reverse ETL use cases specific to their needs.

Some use cases aim to meet the need for real-time data for business teams in the front office, sending transformed data to feature stores for internal dev teams, and furnishing fresh data for enabling automated AI decisions. Other use cases, as discussed above, include enriching SaaS data with a global customer view and continuously syncing warehouses with customer touchpoints.

Tips for Succeeding with Reverse ETL

To make sure that you’re building a high quality Reverse ETL, you should take into consideration the following:

  • Make sure scheduling is precise and highly customizable.
  • Check for consistency in data sync across touchpoints.
  • Ensure enterprise-grade connector quality and check for brittleness.
  • Check monitoring and alerting capabilities.
  • Ensure the pipeline is compatible with a variety of integrations and data sources.
  • Only use tools that ensure security and compliance.

Conclusion

While Reverse ETL comes across as a completely new approach, it’s simply a development on top of the traditional ETL pipeline and its successor, ELT. Reverse ETL has naturally evolved from the need to leverage huge heaps of data into daily actionable insights.

While ETL helps to create an SSOT for the organization, Reverse ETL brings the insights from the SSOT to downstream applications to improve customer experience through real-time analytics.

Meltano is an open source DataOps tool that helps manage all your data tools through a single platform. It allows integration with multiple data sources and maintains high visibility. With Meltano, users can access best-in-class open source tools under one umbrella and leverage over 200 high quality connectors.

Meltano believes in harnessing the power of complementary technologies, such as ETLs and Reverse ETLs, to build healthy and efficient data environments that can be leveraged by multiple customer-facing and backend teams across the organization.

Guest written by Samadrita Ghosh. Thanks Samadrita!

Category: Data and Analytics

Interested to know how the DataOps OS can help with your business or need tips on how to make your data stack work for you?

Fill in the form below and we will get back to you shortly.