Reverse ETL (Extract, Transform, Load), a relatively newer data integration paradigm, operationalizes enterprise data to accelerate digital transformation. Lately, reverse ETL has become an essential part of data management practices, enabling enterprise data teams to reverse the traditional ETL and warehousing process. 

As the name suggests, reverse ETL treats the traditional ETL sources (third-party data sources such as CRMs, database systems, and external files) as targets and traditional ETL destinations (typically a cloud data warehouse such as Snowflake or Google BigQuery) as reverse ETL sources. When enterprise data is available at the disposal of your non-data teams, they can use it effectively to make business decisions. 

There are a few factors that determine the effectiveness of a modern reverse ETL tool. In this article, we’ll discuss five reverse ETL best practices that can unlock the full potential of your modern data stack. In the end, we’ll discuss some questions to clarify the role of reverse ETL in data engineering. Let’s begin.

Table of Contents

  1. Quality Data Connectors and Automated Syncing
  2. Data Security & Regulatory Compliance in Data Engineering Pipelines
  3. Fault-Tolerant Data Engineering Practices
  4. Data Observability and Auditing
  5. Scalable Data Engineering
  6. What’s the Point of Having ETL Workflows if We Are Going to Move Data Out of the Data Warehouse Again?
  7. Is Reverse ETL the Same as ELT?
  8. Leverage No-Code Reverse ETL with to Activate Operational Analytics

1. Quality Data Connectors and Automated Syncing

An organization uses multiple dozen tools daily. Statista reports that in 2021 organizations were using 110 SaaS tools on average, compared to 80 SaaS tools in the previous year. This is where the role of reverse ETL is critical. It combines data from different sources and makes it readily available for your business teams

Reverse ETL tools must provide high-quality data connectors or plugins that can facilitate quick data transfer. When implementing a reverse ETL pipeline, the organizations should consider the following factors:

  • Is the reverse ETL tool compatible with their current business tools like Salesforce, HubSpot, Marketo, MailChimp, etc?

  • Can they connect with the required sources and destinations using reverse ETL connectors?

  • Are reverse ETL connectors easy to integrate and manage?

  • Does the reverse ETL tool provide real-time data transfer for large volumes of data?

Reverse ETL connectors allow organizations to un-silo data. Before reverse ETL, business teams had to ingest data from different sources and process it individually before performing analytics. In contrast, reverse ETL offers a centralized data warehouse that can connect with any third-party business tool to operationalize enterprise data

Additionally, data connectors can automatically sync data from source warehouses to destination tools, giving them a 360-degree view of the enterprise data. For instance, your marketing team receives real-time updates on the latest ad campaign that includes engagement, activity, and click-through rates. They can analyze this data to update their marketing pipeline quickly.

With automated workflows, reverse ETL tools save tons of time, giving more flexibility to your business teams and maximizing profits.

2. Data Security & Regulatory Compliance in Data Engineering Pipelines

Securing data according to regulatory compliance is one of the most critical reverse ETL best practices. Whenever data is involved, all stakeholders, including customers and regulators, are concerned with its security and privacy.

Because they deal with data, Reverse ETL tools are an asset that must be secured. Multiple data touchpoints, dozens of third-party integrations, and continuous data flow from source to destination make reverse ETL tools vulnerable to security breaches. However, robust reverse ETL tools employ effective measures to ensure data safety at the source, in transit, and at the destination.

In particular, reverse ETL tools should follow two data security best practices which are as follows:

  • Regulatory standards: All data coming in and going out of the system must follow the regulatory standards set by GDPR, CCPA, PCI DSS, HIPAA, and SOC 2 protocols. Reverse ETL vendors must comply with these protocols to protect personally identifiable information (PII) and enterprise data. 

  • Data encryption: Reverse ETL tools must employ robust data encryption techniques to secure the data transfer from the data warehouse to third-party business tools is secure. Additionally, reverse ETL tools should secure all data backups and snapshots.

Data engineers who are maintaining reverse ETL tools are also responsible for security governance. They can identify unauthorized data access to datasets, databases, and codes by monitoring access logs and audit trails. They can protect sensitive data by running various security tests on the whole system.

3. Fault-Tolerant Data Engineering Practices

Organizations cannot afford data loss due to any kind of malfunction. Imagine if enterprise data is erased or corrupted. The company would lose millions in revenue because it won’t have relevant data to communicate with its customers or perform its daily data-driven activities. 

Ensuring fault tolerance is one of the most critical reverse ETL best practices. When your data engineering teams deal with millions or potentially billions of rows of data, errors are inevitable. However, mitigating these errors as effectively as possible to minimize damage is vital for the survival of an enterprise.

Detection and recovery are two major components of fault-tolerant systems. Modern reverse ETL tools are equipped with advanced fault-tolerant systems that can proactively catch and correct errors or recover system state in case of network failure or overload.

In fault detection, the reverse ETL tool can use heartbeat detection to periodically check the status of the source and destination. For instance, if the destination tool does not receive a heartbeat signal from the source data warehouse, then connector sync might have a problem. Moreover, intelligent reverse ETL tools can predict faults by analyzing the historical execution of data operations.

A fault-tolerant reverse ETL tool is dependable, reliable, and available. It offers enough redundancy to keep the operations running. If your reverse ETL tool does not offer a robust fault-tolerant architecture, be ready to face some legal and financial troubles.

4. Data Observability and Auditing

Data integration is a complex process that requires careful observation of the entire system. In the data ecosystem, data observability refers to the process of tracking your data health using alerts, notifications, and logs. Data observability is a critical player in the modern data stack that aims to achieve minimal disruption of the data integration process.

Reverse ETL tools should have the capabilities to track five data observability principles which include: 

  • Freshness: Checks if the data going into the third-party business tools is up-to-date.

  • Distribution: Checks if data is formatted correctly and the data values are within accepted ranges. 

  • Volume: Checks if complete data is present and transferred (in terms of size)

  • Schema: Tracks any changes to the data structure and format

  • Lineage: Keeps a historical track record of how the available data was generated up until it is consumed at the destination

Data observability ensures better governance, integrity, and reliability of the data system. Reverse ETL tools should either provide observability features or integrate with external data observability tools to enable audit logs and system rollback capabilities.

Data observability empowers auditing. It enables reverse ETL tools to keep track of any changes in the data flow. Reverse ETL tools powered by data observability and auditing features offer greater transparency in their operations, giving more confidence to the data teams during decision-making.

5. Scalable Data Engineering

Enterprise data volume is snowballing. In 2022, enterprise data volume is expected to reach 2.02 petabytes, compared to 1 petabyte in 2020. While implementing reverse ETL best practices, it is important to note whether the tool can scale reverse ETL pipelines as per business requirements.

Scalability is needed for two reasons: handling large data inflow and processing it at a greater speed. Reverse ETL tools should be able to automatically scale vertically and horizontally, as per requirement.

Reverse ETL pipelines should scale quickly without interrupting daily activities. Scalability depends on the availability of relevant data connectors. If a data connector is unavailable, the reverse ETL tool should be flexible enough to support custom API integrations that are easy to implement, enabling data teams to build and manage their own plugins. 

Moreover, scalability also depends on how the data is transferred, either as a stream or in the form of batches. Both stream or batch data pipelines should be able to manage massive big data inflow while maintaining the same processing and syncing speed. 

What’s the Point of Having ETL Workflows if We Are Going to Move Data Out of the Data Warehouse Again?

Modern data platforms don’t work well with siloed data. When data is gathered in centralized storage like a warehouse, it hinders many data operations, such as evaluating KPIs and metrics, building dashboards, or enabling automation across the organization.

If data is only accessible to your dataOps teams, they will only use it to build data models using SQL, which offers limited use cases. Reverse ETL tools take this data out of the silo and offer it to your business teams which are running day-to-day operations and interacting with business users across various departments of the organization.

Reverse ETL offers many use cases for operational teams. For instance, the sales team would like to monitor a key customer metric or KPI that is usually evaluated in the cloud data warehouse. Reverse ETL can bring this calculated metric to a CRM tool like Salesforce.

Another reverse ETL use case includes marketing teams analyzing customer purchase patterns and ad campaign responses using e-commerce and marketing automation tools. Similarly, reverse ETL can transfer customer data to customer data platforms (CDP) to enable operational analytics for marketing and sales teams.

Is Reverse ETL the Same as ELT?

Just like reverse ETL should not be confused with ETL, it should not be confounded with ELT as well. ELT refers to Extract, Load, and Transform. It extracts data from disparate sources and loads it directly into the data warehouse without performing the data transformation process.

ELT offers an alternate and more effective integration workflow compared to traditional ETL. The data transformation process is performed on an as-need basis within the cloud data warehouse, reducing the time between data extraction and delivery.

ELT is often used with data lakes for storing the bulk of raw data. Data lakes offer more flexibility and scalability if data infrastructure needs to be updated for future use. It enables data teams to transform and optimize raw data in real time as per business requirements. They can use a built-in or external data build tool (dbt) to transform this raw data. DBT tools are SQL enabled, which breaks down the complexity of the data transformation process, including aggregation, normalization, and sorting.

Once the data is gathered in the data lake, reverse ETL can operationalize it. It transfers data from the data lake (or data warehouse) into business intelligence, visualization, and data analytics tools.

Leverage No-Code Reverse ETL with to Activate Operational Analytics

Reverse ETL tools democratize data across the organization and strengthen the modern data stack. It enables data engineering teams to focus on improving data quality rather than building custom connectors and maintaining APIs to support different third-party tools.

With built-in connectors, reverse ETL tools automate data workflows and transfer data in real-time, allowing different operational teams (like customer support, sales, and marketing) to leverage customer data and make data-driven business decisions. offers a user-friendly reverse ETL platform that enables even non-engineers to configure and manage robust data pipelines using 200+ built-in data sources and destinations. It implements reverse ETL best practices to deliver a robust modern data stack for e-commerce. Moreover, this platform provides an intuitive drag-and-drop interface with a no-code experience to set up ETL workflows within a few clicks.

Contact us today to enable intelligent decision-making with our scalable reverse ETL tool.