Imagine a massive server room filled with high-powered computers, each humming with the potential to unlock valuable insights. They’re like encrypted messages, useless without the key to decipher them. This is often the fate of data stored in data warehouses. It's valuable but locked away in an unusable format for everyday business operations. Here’s where reverse ETL processes can help you manage and utilize valuable information. You can use specialized tools to direct the information stored in your data warehouse to your marketing, sales, and other teams. Thus, you can finally understand the encrypted message and put it to use.
In this article, you will explore the top reverse ETL tools of 2024, empowering you to unlock your data’s potential and transform it from a locked server room to a powerful growth engine.
What is Reverse ETL?
Reverse ETL, as the name suggests, is the opposite of the traditional Extract, Transform, and Load process used in data integration. While ETL focuses on bringing data from various sources into a central repository, reverse ETL is about extracting data from the data warehouses.
Here’s a breakdown of the reverse ETL process:
- Extract: Data relevant to a specific use case is extracted from the data warehouse. This data could be insights, aggregated metrics, or any other information that can add value to operational systems.
- Transform: The extracted data might need some transformation to fit the format and requirements of the destination system. This could involve filtering, cleaning, or restructuring the data.
- Load: The transformed data is then loaded into the target operational system. For example, CRM, marketing automation platform, etc.
Benefits of Reverse ETL
Reserve ETL offers several significant benefits for your organizations:
- Reduced Latency in Decision-Making: Traditional ETL processes might introduce delays in data-driven decisions as insights are typically generated in a separate analytics environment. Reverse ETL minimizes this latency by enabling the immediate application of insights to operational systems, promoting faster and more informed decision-making.
- Real-time Data Synchronization: By facilitating data flow back to operational systems, reverse ETL ensures that information is up-to-date in both the analytics and the operational databases. This real-time synchronization enables your organization to work with the most current and relevant data across the entire ecosystem.
- Dynamic Adaptation to Change: As your business requirements evolve, reverse ETL tools provide the adaptability needed to accommodate changes in data sources, schemas, or business rules. This dynamic nature ensures your organization stays responsive and agile in a rapidly changing business.
Top 7 Reverse ETL Tools
Unlock the power of data integration with this roundup of the best 4 reverse ETL tools for you to use in 2024.
Census
Census is a platform that solely focuses on reverse ETL. It boasts a user-friendly interface and powerful features like data partitioning, segment creation, etc. Census simplifies data activation by syncing good data from your warehouse to various business tools. It breaks down silos, combines data across your organization, and ensures consistent, real-time data for everyone.
Key features of the Census include:
- Census syncs insights from the cloud data warehouse to your SaaS tools.
- Its 360-degree profile view empowers sales, marketing, and support teams with fresh and consistent consumer data across all tools.
- Census offers detailed sync logs in your warehouse. This helps you audit logs, troubleshoot and monitor errors as well as create alerts.
Pricing
Census provides three versions of the pricing model—Free, Professional, and Enterprise. The Free version includes one destination, active sync, user set, and workplace. The Professional version costs $350 per month with additional functionalities. The Enterprise version has customized pricing.
Hightouch
Hightouch offers a reverse ETL approach and allows you to streamline the process of syncing customer data from the data warehouse to various business tools and platforms. With its user-friendly interface and automation features, it enables you to leverage your data for personalized marketing and sales.
Key features of Hightouch include:
- Hightouch specifies the consumer data based on individual buyer attributes and behavior to relevant applications like marketing automation platforms or CRMs.
- It adds additional information to existing client data before activation, like purchase history or website behavior, to create a richer customer profile for target actions.
Pricing
The Hightouch platform provides two pricing versions—Free and Business. The Free version gives you a 30-day free trial, and after that, you can update your plan, which is $350 per month. The Business plan is customized, and you only have to pay for what you need. For detailed pricing, you can contact their sales team.
DataChannel
DataChannel is a versatile platform that offers traditional ETL functionalities as well as reverse ETL capabilities. This allows you to extract data from your centralized destination and deliver it to downstream applications for further analysis or action. With its intuitive interface and robust functionalities, it empowers you to leverage your data for analytics, reporting, and decision-making.
Some common features of DataChannel are:
- It provides you with a library of 100+ fully managed connectors, including SaaS apps, databases, warehouses, and more, to leverage your data in minutes.
- DataChannel is built on a scalable and reliable architecture, ensuring you can handle growing volumes of data and maintain data availability.
Pricing
The DataChannel has three pricing methods—Free, Professional, and Enterprise. The Free version comes with two data sources, 50GB storage, and 20 data models. The Professional version comes with additional features and costs $250 per month. The Enterprise version is recommended if you have enormous datasets and comes with customized pricing.
Dataddo
Dataddo offers a fully managed, coding-optimal outlet that is unrivaled in connectivity. While its current selection of reverse ETL destinations is smaller, they are constantly adding new options and can even build custom connectors upon request.
Here are some features of Dataddo:
- Dataddo manages all API changes, proactively monitors and fixes pipelines, and builds new connectors in around ten business days.
- By sending your data directly to a dashboarding app, you can test the validity of any data model on a small scale before deploying it fully in a data warehouse.
Pricing
Dataddo offers four pricing versions—Free, Data to Dashboards, Data Anywhere, and Headless Data Integration. The free version is made freely available for you by its community. The Data to Dashboards and Data Anywhere plans cost $99 per month. Meanwhile, the Headless Data Integration pricing version is customized pricing, and you can contact the Dataddo team for more details.
Rivery
Rivery simplifies data preparation and sends valuable insights from your warehouse to operational systems seamlessly. Its reverse ETL tool is user-friendly, with a simple interface. Highly adaptable, Rivery connects with numerous destinations through APIs, giving businesses complete control over their warehouse data.
Hevo
Hevo is another reverse ETL, making moving data from warehouses to other apps easier. It's all about smooth connections, scalable systems, and changing data to get it ready quickly. With Hevo, you can make sure your data is where it needs to be without a hassle. With Hevo, customers can easily create multiple workspaces under the same domain name. This feature allows for better organization and collaboration within teams.
Segment
Segment is good at managing and using customer data. It collects and shares customer data with different apps right away. It's great at keeping everything up-to-date and making sure all your customer info is in one place. But, when it comes to changing data in fancy ways, it might not have all the cool features that other reverse ETL do. Segment gathers data from multiple sources, not just one, making sure businesses have a comprehensive view of their customer information. This means they can collect data from various places like websites, mobile apps, and more.
While these reverse ETL tools offer valuable functionalities, you must have some prior coding knowledge before using these tools. Additionally, managing and maintaining multiple data pipelines across different tools can add complexity. Moreover, ensuring data quality across various destinations requires ongoing vigilance. This is where Airbyte steps in! Its error handlers and schedule data sync automate your data flow between the warehouse and operational tools. Thus, your data quality is never compromised and the flow of information occurs smoothly.
Things to Consider When Choosing a Reverse ETL Tool
Ease of Use
Look for a reverse ETL tool with a user-friendly interface and intuitive workflows. Consider whether the tool provides drag-and-drop functionality, pre-built connectors for popular data sources and destinations, and visual tools for data transformation.
Scalability
Consider the scalability of the reverse ETL tool to accommodate your current and future data needs. Assess whether the tool can handle large volumes of data efficiently and seamlessly scale as your data requirements grow.
Data Transformation Capabilities
Evaluate the tool's data transformation capabilities, including its ability to clean, enrich, and manipulate data as it moves from source to destination. Consider whether the tool supports a wide range of transformation functions, such as filtering, aggregating, and joining data sets, as well as more advanced operations like machine learning-based data processing.
Integration Flexibility
Assess the tool's integration flexibility and compatibility with your existing data infrastructure. Look for a reverse ETL solution that offers seamless integration with your preferred data warehouses, databases, BI tools, and other systems. Consider whether the tool supports both batch and real-time data integration, as well as APIs and SDKs for custom integrations with proprietary or legacy systems.
Data Governance and Security
Prioritize data governance and security features when choosing a reverse ETL tool to ensure compliance with regulatory requirements and protect sensitive information. Evaluate the tool's capabilities for data lineage tracking, access control, encryption, and compliance auditing. Look for certifications such as SOC 2, GDPR, and HIPAA compliance, which demonstrate the tool's commitment to maintaining confidentiality, integrity, and availability of your data.
Beyond the Warehouse Walls: Airbyte Empowers Reverse ETL Customization
Airbyte is a data integration platform focused on simplifying the process of building and managing data pipelines. It provides a vast library of 350+ pre-built connectors, allowing you to easily extract data from various sources like databases, SaaS platforms, etc. Airbyte provides you with transparency, cost-effectiveness, and a high degree of connector customization.
Here are some features of Airbyte:
- Destination Flexibility: Airbyte isn’t limited to loading data into data warehouses. You can also connect to operational systems like marketing or sales platforms, and CRMs. This allows you to push data back to these systems for further analysis and action.
- Connector Development Kit: Airbyte allows you to build customized connectors in ten minutes for your specific reverse ETL needs using its Custom Connector Kit (CDK). This provides greater flexibility compared to closed-source platforms with limited pre-built options.
- Developer-friendly Interfaces: With Airbyte, you can manage and handle your data pipelines in four convenient ways—UI, API, Terraform Provider, and PyAirbyte. Opt for the UI if you prefer a visual approach, or choose the API, and Terraform Provider if you’re inclined toward programming. PyAirbyte is Airbyte’s open-source Python library that includes Airbyte connectors and can be accessed using Python programming.
- Security: Airbyte prioritized data protection by adhering to industry standards. It has successfully completed a SOC Type II audit, verifying the effectiveness of its controls for safeguarding your data. Its ISO 27001 certification also ensures a robust data Security Management System is in place. Thus, you can be assured of your data pipeline’s security at all times.
Pricing
Airbyte offers four versions—Open-Source, Cloud, Self-managed, and Powered By Airbyte. The open-source is free and powered by its community. Its cloud version is based on pay-as-you-go usage, allowing you to pay only for the sources you use. The self-managed and Powered by Airbyte versions have customized pricing.
Conclusion
Choosing the right tool can be overwhelming, with so many options available. This guide has explored four popular reverse ETL tools for you, each offering unique strengths. You can quickly transform and process your data by selecting the right reverse ETL tool according to your specific requirements.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What is ETL?
ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.
What is ELT?
More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:
- Faster processing times and loading speed
- Better scalability at a lower cost
- Support of more data sources (including Cloud apps), and of unstructured data
- Ability to have no-code data pipelines
- More flexibility and autonomy for data analysts with lower maintenance
- Better data integrity and reliability, easier identification of data inconsistencies
- Support of many more automations, including automatic schema change migration
Here is our recommendation for the criteria to consider:
- Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
- Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
- Ability to build new connectors: all data integration solutions support a limited number of data sources.
- Support of change data capture: this is especially important for your databases.
- Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
- Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
- Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ?
- Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
- Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
- Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
- Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.
1. Airbyte
Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.
What's unique about Airbyte?
Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..
Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.
Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.
2. Fivetran
Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.
Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.
What's unique about Fivetran?
Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.
Here are more critical insights on the key differentiations between Airbyte and Fivetran
3. Stitch Data
Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.
Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.
What's unique about Stitch?
Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.
Here are more insights on the differentiations between Airbyte and Stitch, and between Fivetran and Stitch.
Other potential services
Matillion
Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.
What's unique about Matillion?
Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.
Here are more insights on the differentiations between Airbyte and Matillion.
Airflow
Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow?
Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow.
Talend
Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.
What’s unique with Talend?
What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.
Pentaho
Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.
What is unique about Pentaho?
What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.
However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.
Informatica PowerCenter
Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.
Microsoft SQL Server Integration Services (SSIS)
MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.
Singer
Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
Rivery
Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
HevoData
HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.
Meltano
Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes, or Airbyte Cloud which you can try here, free for 14-days.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Frequently Asked Questions
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: set it up as a source, choose a destination among 50 available off the shelf, and define which data you want to transfer and how frequently.
The most prominent ETL tools to extract data include: Airbyte, Fivetran, StitchData, Matillion, and Talend Data Integration. These ETL and ELT tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.