The Ultimate Guide to Reverse ETL: Everything You Need to Know and Master

Reverse ETL refers to the process of transforming data within a data warehouse and subsequently loading it into a third-party system to facilitate actions.

In the current data-centric economy, ETL transcends its traditional role of data centralization within warehouses. More frequently, teams leverage ETL to transmit data from warehouses to external systems. Termed “Reverse ETL,” this approach is rapidly gaining prominence as a fundamental component of technology data frameworks.

Reverse ETL enables the feeding back of data, including insights and analytics, to systems for real-time utilization. For example, a company can utilize Reverse ETL to extract customer segmentation data from its dedicated analytics platform and subsequently reload it into its CRM system. This approach grants decision-makers and sales teams immediate access to real-time customer insights. Moreover, it empowers them to tailor customer interactions and enhance the efficiency of their sales campaigns. This comprehensive guide provides your data team with everything they need to know about Reverse ETL.

How does Reverse ETL work?

The mechanics of Reverse ETL closely resemble those of standard ETL. However, a key distinction lies in Reverse ETL’s retrieval of data from a data warehouse or analytics platform, transforming it into a format compatible with existing operational systems or other applications, and subsequently loading it back into those systems. Alternatively, Reverse ETL can be conceptualized as a reverse flow of data—a process akin to ETL but in reverse.

Initially, a Reverse ETL process retrieves pertinent data from a data warehouse or platform. This extracted data may encompass product details, customer data, and other pertinent business insights. Subsequently, the retrieved data is transformed to conform to the specific operational prerequisites within the intended system. This transformation stage entails actions such as filtering, reformatting, or aggregating the data.

Finally, the transformed data is loaded back into designated operational systems or applications for further utilization. This particular process facilitates real-time access to dependable and valuable insights, empowering data teams and decision-makers to make informed decisions and take actions based on the most up-to-date information.

Reverse ETL fundamentally reimagines the standard data transfer protocol for companies. Employing the same foundational concepts, Reverse ETL operates in the reverse direction, diverging from the conventional path of extracting data from a single source and loading it into a warehouse.

ETL vs. Reverse ETL: Understanding the contrast

To grasp reverse ETL, let’s revisit the basics of traditional ETL. Extract, Transform, and Load (ETL) is a data integration approach that retrieves raw data from sources, processes the data on a secondary server for transformation, and subsequently transfers it into a target database.

Reverse ETL technoclap

In recent times, with the emergence of cloud data warehouses, the extract, load, transform (ELT) approach has gradually replaced ETL. Unlike ETL, ELT doesn’t necessitate data transformation prior to loading. Instead, ELT directly loads raw data into a cloud data warehouse. Data transformations are then conducted within the data warehouse using methods such as SQL pushdowns, Python scripts, and other coding techniques.

ELT technoclap

Data from external systems, such as databases like Oracle and MySQL, and business apps such as HubSpot and Salesforce, are transferred into target data warehouses via both ELT and ETL processes. However, in Reverse ETL, the data warehouse serves as the source rather than the destination. In this scenario, a third-party system becomes the destination. Reverse ETL entails extracting data from the data warehouse, transforming it within to adhere to the formatting requirements of the third-party system, and then loading the modified data into the third-party system for further use.

Reverse ETL Process

Reverse etl process technoclap

The term used is reverse ETL instead of reverse ELT because data warehouses lack the capability to directly load data into a third-party system. The data needs to be transformed first to align with the formatting requirements of the third-party system. However, this process differs from traditional ETL because data transformation takes place within the data warehouse itself. There isn’t an intermediate processing server responsible for transforming the data.

Here’s an example: Suppose a Tableau report includes a customer lifetime value (LTV) score, but this data, formatted for Tableau, cannot be directly used in Salesforce. In this scenario, a data engineer utilizes SQL-based transformations within Snowflake to extract the LTV score from the report data, format it to align with Salesforce requirements, and then insert it into a designated Salesforce field. This enables sales representatives to access and utilize the LTV information effectively.

Reverse ETL Use Cases

In the current data-centric business environment, Reverse ETL has proven its practical utility across various sectors. Notable applications of Reverse ETL include the following:

Marketing Campaign Optimization: Marketing professionals can utilize Reverse ETL to enhance the efficiency of their campaigns. With Reverse ETL, marketers gain real-time insights into the performance metrics of their campaigns. This enables them to make informed adjustments, optimize targeting processes, and refine marketing strategies effectively.

Real-time Sales Insights: Sales teams can leverage Reverse ETL to retrieve sales data from analytics platforms and reintegrate it into their CRM systems. This integration enables sales representatives to access dependable, current information regarding customer behavior, purchasing trends, and sales performance directly from their CRM systems. This simplifies the process of making data-driven decisions and identifying cross-selling opportunities while ensuring reliability.

Customized Consumer Engagement: Reverse ETL empowers e-commerce companies to implement real-time customization of consumer recommendations. Data operators extract consumer browsing and purchase histories from a data warehouse, adjust them, and then reload them into websites or applications. This capability enables companies to provide their customers with tailored promotions, personalized product/service recommendations, and more.

The Need for Reverse ETL

here are numerous compelling reasons why businesses should adopt Reverse ETL, many of which directly impact their customer relations. Regardless of their scale or scope, businesses require Reverse ETL to connect their data with operational systems effectively.

Businesses should implement Reverse ETL to convert analytical insights into actionable data, facilitate real-time decision-making, seamlessly integrate their systems, enhance customer experiences, and enhance overall operational efficiency.

Here are some key reasons why Reverse ETL is indispensable for businesses:

Actionable Insights: Reverse ETL enables businesses to convert valuable data insights from analytics platforms into actionable information that operational systems can leverage. By loading pertinent data back into the business’s operational systems, teams can make more informed decisions and respond promptly. Consequently, this leads to enhanced efficacy.

Real-time Decision-Making: Businesses can depend on Reverse ETL to access recent, up-to-date data from analytic platforms that can be seamlessly loaded into operational systems. This ensures teams have access to current insights and are equipped to respond promptly to market changes, customer demands, and emerging opportunities.

Smooth Data Integration: Reverse ETL serves as an ideal solution for achieving seamless data integration across diverse systems and applications within an organization. This process enables businesses to synchronize data across various platforms, ensuring accuracy and coherence.

How Does Your Data Infrastructure Use Reverse ETL?

The Reverse ETL process occupies a crucial position in the data infrastructure by connecting central data repositories or data warehouses with operational applications and systems. In a successful data infrastructure, Reverse ETL works alongside traditional ETL processes, serving as a facilitator in the extraction and transformation of data to and from operational systems within an organization.

This data movement facilitates near-real-time or real-time updates and integrations into various applications, enabling businesses to utilize their existing data more accurately and efficiently. Within a comprehensive data infrastructure, Reverse ETL empowers data teams to make informed decisions, personalize, and automate diverse data processes. Furthermore, Reverse ETL enhances the overall data environment by ensuring bidirectional data flows between storage and usage points.

Reverse ETL transforms and enhances data extracted from analytics platforms, rendering it more usable and compatible with various operational systems. It constitutes a crucial component of a comprehensive data infrastructure, encompassing tasks such as data cleansing, aggregation, reformatting, and/or application of specific business rules to align data with the requirements of target systems.

Comparing Reverse ETL with Other Technologies

  • Reverse ETL distinguishes itself from other processes by prioritizing bidirectional data flow, facilitating real-time decision-making, and seamlessly integrating analytics into operational systems. While standard ETL, data integration platforms, data pipelines, and data replication technologies may belong to a similar category, they serve distinct purposes.
  • Conventional ETL technologies are designed to load data into a data warehouse platform for further usage and analysis after extracting it from several sources and transforming it. On the other hand, Reverse ETL loads data back into operational systems after retrieving it from analytics platforms. Reverse ETL incorporates insights into operational procedures, whereas ETL makes data analysis and reporting easier.
  • With a primary focus on streaming data between applications and systems, data integration solutions like Apache NiFi enable data mobility and synchronization between data systems. Reverse ETL, on the other hand, places more emphasis on bidirectional data flow between operational systems and analytical platforms.
  • Data replication technologies, such as Change Data Capture (CDC) mechanisms, are designed to maintain synchronized copies of data across different data systems. While data replication can ensure data consistency, it is commonly employed for scenarios involving disaster recovery and distributed database architectures.

What Effect Does Reverse ETL Have?

Reverse ETL operationalizes data throughout a firm by pushing it back into external systems like business apps. Any team, including sales, marketing, and product, may access the data they require within the platforms they use thanks to reverse ETL. Although there are many uses for reverse ETL, here are a few examples:

  • Integrating Zendesk with internal support channels to prioritize customer care.
  • Transferring client information to Salesforce to enhance the sales process.
  • Utilizing HubSpot to merge sales, support, and product data to craft personalized marketing campaigns for clients.

Even within companies equipped with a cloud data warehouse, data may not always reach the appropriate users. Reverse ETL addresses this issue by directly pushing data into the applications used by line-of-business (LOB) users. In certain companies, teams may already have access to the required data through BI reports. However, much to the frustration of BI developers, these reports are frequently underutilized.

What teams truly desire is the ability to access data within the systems and processes they are accustomed to. This is precisely what reverse ETL facilitates. With reverse ETL, business users can effectively utilize data in an operational capacity. Teams can take action on the data in real-time and leverage it to make critical decisions.

Reverse ETL can streamline data automation across a company. It aids in eliminating manual data processes, like CSV pulls and imports, typically associated with data tasks. At times, reverse ETL serves as a component within a broader data workflow. For example, if you’re constructing an AI/ML workflow atop your Databricks stack, reverse ETL can push formatted data into the sequence. Additionally, companies are increasingly integrating reverse ETL into in-app processes, such as syncing production databases.

Which Reverse ETL solution is correct?

A team has the option to either build or purchase an ETL solution. Those who opt to build an ETL solution must develop data connectors — the link between the data source and the data warehouse — from the ground up. This development process can span weeks or even months, depending on the capabilities of the development team, and can hinder scalability, consume development resources, and impose long-term maintenance obligations on the system. For these reasons and others, data teams frequently explore SaaS ETL platforms.

SaaS ETL platforms typically come equipped with “pre-built” data connectors. The quantity of connectors varies among providers, but platforms commonly provide “plug-and-play” ETL connectors for popular data sources. However, these data connectors are designed to extract data from third-party systems and load it into data warehouses. In contrast, Reverse ETL data connectors operate in the opposite direction: they must extract data from data warehouses and load it into third-party systems.

In simpler terms, an ETL data connector is distinct from a reverse ETL data connector. Therefore, when ETL platforms advertise ETL data connectors, it doesn’t automatically imply the availability of a reverse ETL data connector. In reality, it’s quite common for teams to adopt an ETL platform and still need to construct reverse ETL connectors on the backend.


What is the difference between ELT, ETL and reverse ETL?

ETL and ELT both move data from third-party systems, such as business applications (HubSpot, Salesforce), and databases (Oracle, MySQL), into target data warehouses. However, in reverse ETL, the data warehouse serves as the source, while the target is a third-party system.

What is the difference between reverse ETL and CDP?

While Customer Data Platforms (CDPs) and Reverse ETL tools share some similar functionalities, they serve different business needs. CDPs primarily concentrate on consolidating customer data to create a unified view, whereas Reverse ETL emphasizes operationalizing analytical data for third-party applications, facilitating complex data flows across various tools.

What is the purpose of reverse ETL?

A reverse ETL tool retrieves up-to-date data from the data warehouse, performs transformations on it, and then loads it into an operational system or application. This process is utilized in numerous use cases where business users seek to employ transformed data or the outcomes of data modeling in their preferred applications.

Is ELT replacing ETL?

The particular use case will determine whether ELT or ETL is preferred. Businesses handling large amounts of data choose ELT, while those processing data from on-premises to the cloud still prefer ETL.