Want to activate raw data in your company's data warehouse? Then you need reverse ETL.
In the world of cloud computing and data management, ETL and reverse ETL are two related but distinct processes of moving data. The main difference between them, as their names imply, is the direction of data flow.
Reverse ETL increases the functionality of your data sets and the teams that use them.
In this guide, we'll dive deeper into what reverse ETL is, how it differs from conventional ETL, and the benefits and challenges of implementing it into your data integration strategy.
ETL (Extract, Transform, Load) moves data from external applications like Salesforce, HubSpot, and Twilio into a central data warehouse.
Conversely, reverse ETL takes data directly from the warehouse and pushes it back out to those individual applications and software.
ETL data integration involves three main functions: extracting data from original sources, transforming it in a standardized way, and then loading it into a single data warehouse.
This process has two different approaches: conventional ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). The difference between these methods is how they tackle data integration.
ETL is an older method ideal for complex data transformations, while ELT offers more flexibility. With ELT, data is transformed within your warehouse after it's loaded, though it requires more time to process that data.
ETL processes and tools like Portable or Fivetran are used to centralize data in a single location, helping teams more effectively analyze the information to improve their processes and decision-making.
Reverse ETL is essentially the same process as ETL, but as the name implies, reversed. Also known as data unloading or extraction, reverse ETL copies data from the data repository and pushes it out to third-party platforms and SaaS applications.
The reverse ETL process aims to bring stored data back into operational systems, making it more accessible so that the right employees in a company can understand it and take action on it.
Reverse ETL is a relatively new concept that's emerged from a growing need for real-time integration in modern data-driven companies.
Reverse ETL involves syncing data from a central "single source of truth" data warehouse and loading it into third-party platforms like CRMs, automation software, and business intelligence tools.
Think of reverse ETL as the opposite of an ETL/ELT workflow: data is taken from a data warehouse and pushed downstream into individual tools.
The reverse ETL process involves identifying a data source, using connectors to extract the data, and then transforming it. From there, the data will be loaded and periodically synced to ensure it's accurate and up-to-date across all operational tools.
We can break down the reverse ETL process into these steps:
The first step of reverse ETL involves identifying a data source — the centralized system where data is stored (your company's single source of truth).
For your company, it might be a data warehouse, data lake, customer data platforms (CDPs), or another type of centralized data store.
Common choices include managed cloud platforms like Snowflake, BigQuery, Redshift, and open-source databases like PostgreSQL.
After the source is identified, data connectors will need to be set up between the source and target applications to make it possible to extract the data.
Luckily, most reverse ETL tools have built-in connectors for ingesting most common data sources.
Once data is extracted from its central source, it needs to be transformed to match the data model formats of each target application.
Most reverse ETL tools have built-in transformation capabilities to take care of this for you.
The next step is identifying the target applications, platforms, and tools to which the data will be pushed out.
Data catalogs help retain the proper data schema and description of data sources across multiple sources and targets.
Data connectors are essential for bidirectional data sync.
Once the data is converted and structured in a usable format, reverse ETL tools deliver it by pushing it out to the target applications.
Delivery can be done via API integrations, webhooks, or file transfer protocols, depending on your specific method.
Reverse ETL tools provide value to companies by moving cleaned, transformed data back into individual tools and platforms where it'll actually be used.
This allows business users to access data via the tools they're already accustomed to. Teams across departments — not just data teams — can easily understand, analyze, and take action on the data in front of them.
Reverse ETL offers several benefits as part of a company's data integration strategy:
Reverse ETL enables real-time data integration from a cloud warehouse to whatever individual tools and applications your company is using to operate. This makes decision-making easier since it allows you to act based on the most up-to-date information.
It improves data accuracy and consistency between your data warehouse and all target applications, eliminating the need for manual data entry and reducing the risk of error.
It eliminates data silos. By providing a way to distribute data between teams and departments using various business applications, your ETL architecture unites data that would otherwise stay siloed in a data warehouse or SQL database.
Reverse ETL helps streamline data processing and analysis by enabling companies to quickly deliver key information to tools across departments, reducing the time it takes to analyze, gain insights, and take appropriate action on data.
It provides enhanced data for non-technical users, meaning employees across departments will be more empowered to understand and act on it.
Reverse ETL tools can handle large data sets and support a wide range of target applications, making it a flexible and scalable solution for most companies.
Reverse ETL can help you leverage your company's data by providing real-time integration between your warehouse and business apps to support operations and guide decision-making.
By breaking down data silos, data becomes more accessible to teams, driving positive action and fostering business growth.
While reverse ETL might seem like the end-all solution for making data more accessible company-wide, it also poses its own challenges.
Here are some of the common issues companies face when implementing it into their data pipeline:
Reverse ETL can result in data quality issues if the data in the centralized warehouse is inconsistent. This can lead to inaccurate information being delivered across many target applications.
Integration with existing data tools and platforms can be complex and time-consuming. You should be prepared to invest in tools and resources to manage the integration process.
Because reverse ETL involves moving sensitive information between systems and applications, it can pose security and privacy risks if you don't ensure data is properly secured.
Some reverse ETL tools lack compatibility with certain data sources or target applications your company uses. You'll need custom connectors to integrate with that software in these cases.
Reverse ETL can be a powerful tool for businesses that want to improve their data activation, automation, and accuracy.
Reverse ETL can benefit different industries as well as multiple departments within a single organization.
Reverse ETL can play a significant role in customer relationship management (CRM) by delivering customer data to CRM tools like Salesforce or Zendesk.
It can provide sales teams with insights into customer behavior, like product usage, that they can then use to inform their sales strategy.
It can also deliver metrics on customer lifetime value, encouraging businesses to take steps to increase customer loyalty.
Conventional ETL generates complex customer information from several data points.
Reverse ETL ensures the marketing team can access updated, simplified versions of this data.
A reverse ETL pipeline can deliver that customer data to automation tools like HubSpot or Keap, enabling companies to personalize their marketing campaigns.
Reverse ETL enables People Ops to measure employee engagement and identify areas for improvement.
It can do this by integrating employee data such as time and attendance, performance management, and company turnover.
This can also help pinpoint opportunities for training and development, leading to increased team productivity and retention in the long run.
Reverse ETL can load data for inventory management from various sources, including eCommerce platforms, point-of-sale systems, and warehouse management systems.
Data can also be delivered to inventory tracking tools such as Fishbowl to track levels in real-time and avoid over or understocking.
Business users can analyze sales trends and identify patterns that impact revenue by delivering data from your warehouse to sales analysis tools.
Reverse ETL can integrate sales data from several sources, ensuring that your revenue forecasts rely on reliable and up-to-date sales data.
Reverse ETL can extract information like product usage data, customer experience metrics, and even customer support tickets.
This data, imported from aggregated data in a data warehouse, can help product development teams identify usage patterns and pain points.
Teams can then draw on these insights to decide on new features, optimize their products, and increase customer success.
The modern data stack (MDS) is a suite of tools used to organize, store, and transform data. This ecosystem emerged primarily due to the rise of the cloud data warehouse.
Reverse ETL plays a vital role in the modern data stack by bridging the gap between your data warehouse and operational business tools.
At a basic level, it allows data teams to replicate and transform data to gain insights for decision-making. It can also power more complicated data pipelines (like machine learning models and real-time production systems).
Reverse ETL is critical in MDS infrastructure because it effectively "unlocks" data.
Reverse ETL complements traditional ETL/ELT processes by offering a way to extract data from a centralized repository.
It takes information that would otherwise be trapped in a silo and pushes it out to third-party applications where it's more accessible to non-technical users.
This enables businesses to analyze real-time dashboards, BI tools, and other platforms to leverage data insights for driving business decisions.
In the modern data stack, reverse ETL solves a core problem: helping you activate your data. Rather than collecting data and letting it sit in a warehouse indefinitely, reverse ETL allows you to take a more proactive approach by using it for operational analytics.
While ETL and reverse ETL are valuable tools for companies, reverse ETL is critical to operationalizing data to business teams — not just analysts and data engineers. It complements conventional ETL processes by extracting data from a central hub and delivering it to third-party systems and the groups that use them.
Creating an ETL strategy might feel overwhelming, but it all comes down to the specific needs of your company. Look for a tool that supports and integrates with your current data infrastructure.
Well-known reverse ETL tools integrate with popular business applications, but Portable is one of the most accommodating data integration tools for companies using less-popular data sources. Not only does it have over 300 built-in connectors, but the team also creates custom connectors on request.