11 ETL tools to consider in 2025

ETL, or Extract, Transform, Load, is fundamental in the realm of data analytics. It is a process achieved through a single tool or set of tools that, first, extracts data from various sources like databases, spreadsheets, or even the internet. Then, it skillfully transforms the data, cleans it up, reorganizes it, and ensures it’s ready for analysis. Finally, ETL tools load this refined data into a central location, such as a data analytics platform, where businesses can readily access and analyze it. 

ETL is the essential workhorse that guarantees data used for vital decisions is accurate, consistent, and prepared for action. These tools enable businesses to harness the full potential of data analytics.

What are ETL tools?

ETL tools are like specialized assistants in the world of data analytics. They are software applications designed to streamline and automate the Extract, Transform, Load (ETL) process. These tools make it easier to pull data from diverse sources, such as databases, files, or web services. Then, they provide  functions to transform the data, so it’s clean, consistent, and ready for analysis that combines data across different sources. Finally, ETL tools facilitate the load process, efficiently moving the transformed data to a centralized location, like a data warehouse, business intelligence (BI) platform, or data-specific applications.

Types of ETL tools

There are various types of ETL tools tailored to different business needs and budgets.

• Enterprise ETL tools: These are robust, commercial ETL solutions offered by companies like Informatica, Domo, and Microsoft SSIS. They are known for their comprehensive features, scalability, and support, making them ideal for large organizations. These tools specialize in working with hundreds to thousands of data sources and support complex data integration needs.
• Open-source ETL tools: Open-source tools like Apache NiFi and Talend provide flexibility and cost-effectiveness. They are favored for their community-driven development, allowing users to modify and extend functionalities. These tools are suitable for businesses seeking budget-friendly options and customization.
• Cloud-based ETL tools: Cloud services like AWS Glue and Google Dataflow offer ETL capabilities as a service. They are highly scalable, serverless, and eliminate the need for infrastructure management. Cloud-based ETL tools are ideal for businesses operating in cloud environments, offering easy integration with already-established cloud data storage and analytics services.
• Custom ETL tools: Some organizations develop custom ETL solutions tailored to their specific needs. These tools are designed in-house and offer precise control over data integration processes. Custom ETL tools are suitable for unique business requirements but often require significant development effort and ongoing maintenance. Often, organizations that work extensively with highly sensitive data like personal health information (PHI) will utilize custom internal ETL tools to better manage security and specific transformation requirements. 

How do ETL tools work?

ETL tools follow a three-step process, and it’s all in the name — extract, transform, load.

Extraction

It’s difficult to find a modern organization that only uses one source of data, and many organizations use multiple data analysis tools, too. The first step in the process is extracting data from its source. Sources include but certainly are not limited to: 

• Sales and marketing applications
• Data storage platforms
• Analytics tools
• Data warehouses
• Mobile apps and devices
• On-premise data storage
• Cloud data storage
• Databases
• CRM systems

ETL tools gather raw data, structured and unstructured, into a single location and consolidate it.

Transformation

The second step in the ETL process applies an organization’s rules and regulations to the data to make it meet requirements and be easily accessible. The transformation process includes: 

• Cleaning the data to resolve inconsistencies and missing values.
• Standardizing the data to a predetermined format.
• Excluding or discarding redundant data. 
• Removing unusable data from the data set and flagging any anomalies. 
• Organizing data by type. 

This step is arguably the most important part of the ETL process because it improves the quality and integrity of the data an organization collects. 

Loading

Last but not least, transformed data is loaded into a new destination. This could be a solution like Domo or a standard data warehouse. Depending on the ETL tool, data may be loaded in one large batch or at scheduled intervals. 

A word on extract, load, transform (ELT) methodology

Extract, load, transform (ELT) tools have the same purpose as ETL tools but the process is slightly different. ELT loads data into the central repository immediately after extraction instead of waiting to transform it. This approach is becoming popular with the rise of cloud storage instead of on-premise solutions, but ELT is still a relatively new process and tools are still evolving. Some organizations need both ETL and ELT tools for gathering data.

How to choose an ETL tool

When selecting an ETL platform for your business, several key factors should guide your decision-making process. These include considering the types of data sources you need to integrate, evaluating the costs associated with the platform, and assessing its capabilities to ensure it aligns with your specific data integration requirements.

Data Sources

Before choosing an ETL platform, it’s essential to identify the variety of data sources your business relies on. These sources could range from databases and spreadsheets to cloud-based applications and web services. Ensuring that your chosen platform can effectively connect and extract data from these sources is crucial for a seamless data integration process. Compatibility with your existing data ecosystem is paramount to prevent integration challenges down the road.

Cost

Evaluate the total cost of ownership, including licensing fees, maintenance, and potential scalability expenses. It’s essential to strike a balance between your budget constraints and the capabilities of the platform. Some platforms offer cost-effective and open-source options, while others provide comprehensive features at a higher cost. Choose a pricing model that aligns with your business’s financial resources and growth projections.

Capabilities

Look for features such as data transformation and cleansing tools, scalability to handle increasing data volumes, support for real-time or batch processing, and compatibility with data warehousing and analytics tools. Additionally, consider user-friendliness, as ease of use can significantly impact your team’s productivity. Prioritize capabilities that directly address your data integration requirements, both in terms of current needs and future scalability.

Top 11 ETL tools in 2024

Ready to see some of the top options out there? Here is a list of some of the top ETL tools on the market. Some of these tools are standalone ETL tools, others are part of a larger suite of data tools. We’ve listed these here to give you a chance to compare the best ETL tools for your specific needs. 

1. Domo

Domo is a cloud-based business intelligence and data integration platform that streamlines ETL processes to deliver real-time insights and data visualization. Domo provides ETL tools for novices who aren’t comfortable using SQL with their drag-and-drop Magic ETL tool. But they also have advanced features that allow more technical users to do advanced data transformation.

Key features: Domo offers pre-built data connectors to various sources. All users need to do is connect their data source to the Domo platform and the connector will automatically perform the ETL process. Domo is also a full data lifecycle tool, supporting data analysis from connecting, transforming, analysis, through sharing insights. Domo provides a user-friendly interface and allows users from both technical and non-technical backgrounds to easily use the platform.
Pros: Thousands of pre-built connectors make the ETL process easy.
Cons: For someone just looking for ETL, Domo likely has more tool capabilities than they’re looking for.

2. Talend

Talend, a Qlik company, is also an end-to-end data platform that offers a comprehensive suite for data integration, transformation, and quality. In comparison to Domo’s simple robust integration options, Talend requires users to build a custom data pipeline for each data source. However, once the pipeline is built, users have flexible and powerful options to use their data however they need it.

Key features: Provides a wide range of connection abilities, powerful data transformation capabilities, and a user-friendly graphical interface.
Pros: Strong community support; rich feature set; supports big data and cloud integration.
Cons: Some advanced features may require a paid version; complex workflows can be challenging to manage.

3. Informatica

Informatica is an enterprise-grade ETL tool known for its data integration, data quality, and data governance solutions. It has been named a leader by Gartner for data integration. 

Key features: Offers a comprehensive suite for data integration, data quality, and data governance, and supports cloud and on-premises deployments.
Pros: Enterprise-grade capabilities; strong data quality features; cloud integration; scalable.
Cons: Though users can start out with a free version, using the enterprise-level tools come with a higher cost. The product may be too complex for smaller organizations.

4. Microsoft SQL Server Integration Services (SSIS)

Microsoft SQL is a Microsoft ETL tool that comes with SQL Server, used for data integration and transformation.

Key features: Tight integration with Microsoft ecosystem, visual design interface, supports various data sources.
Pros: Included with SQL Server; user-friendly for Microsoft users; strong data transformation capabilities.
Cons: Windows-centric; may require licensing for SQL Server.

5. Microsoft Azure Data Factory

Microsoft Azure is a cloud-based ETL service that automates data movement and transformation across various sources.

Key features: Includes a visual design interface, pre-built connectivity to on-premises and cloud data sources, and robust data transformation capabilities.
Pros: Strong integration with Azure and other Microsoft products; scalable; supports complex workflows.
Cons: May require a learning curve for non-Microsoft users; it will get expensive the more you use cloud resources.

6. Google Dataflow

Google Dataflow is a fully managed stream and batch data processing service that can be used for ETL.

Key features: Serverless architecture, supports real-time and batch data processing, integrates with Google Cloud ecosystem.
Pros: Easy integration with Google Cloud; scalability; real-time capabilities.
Cons: Tied to Google Cloud Platform; may have associated costs.

7. SAP

SAP is an ETL tool that supports comprehensive data integration, transformation, and quality management for both cloud and on-premises environments. Given the size and scale of its parent company, this tool is most appropriate for enterprise-grade users.

Key features: Comprehensive data integration, advanced data transformation, and strong data quality management.
Pros: Enterprise-grade capabilities; strong integration with SAP products; extensive data quality features.
Cons: Higher cost for enterprise features; may be complex for smaller organizations or users not familiar with SAP.

8. Matillion

Matillion is a cloud-native ETL tool designed for data integration, transformation, and orchestration in cloud environments.

Key features: Native connectors for cloud data warehouses; intuitive interface; data transformation capabilities.
Pros: Cloud-native; easy to use; scalable; strong support for cloud data warehouses.
Cons: Costs may vary based on usage; not ideal for on-premises deployments.

9. Fivetran

Fivetran is a fully managed ETL service that automates data extraction, transformation, and loading with pre-built connectors for a wide range of sources.

Key features: Automated data schema management, pre-built connectors, and seamless data integration with major data warehouses.
Pros: Quick and easy setup; minimal maintenance required; reliable data sync.
Cons: Limited customization options for advanced users; pricing can be high for large volumes of data.

10. CloverDX

CloverDX is a data integration platform known for its data transformation, data quality, and data migration capabilities.

Key features: Visual data transformation; data quality features; supports various data sources.
Pros: User-friendly; versatile data transformation; strong data quality features.
Cons: Licensing costs; may require expertise for complex transformations.

11. Pentaho Data Integration

Pentaho Data Integration, owned by Hitachi Ventara, is an open-source ETL tool for data integration and transformation.

Key features: Visual design interface; supports various data sources; offers strong transformation capabilities.
Pros: Open-source; user-friendly; extensible; supports big data integration.
Cons: Limited support options; may require development effort for advanced features.

ETL tools are indispensable in the realm of data integration; they empower businesses to efficiently gather, transform, and load data from diverse sources for insightful decision-making. When you’re ready to select the right ETL platform, remember to carefully consider your data sources, cost constraints, and required capabilities. 

And while there are many excellent ETL solutions available, such as Domo’s range of powerful features and benefits, make sure your chosen ETL tool aligns with your organization’s unique data integration needs, ensuring that your data becomes a valuable asset in your pursuit of success.

ETL tools FAQs

Have additional questions about ETL tools or how to choose the best ETL platform? We’re here with answers.

What are the benefits of an ETL tool?

An ETL (Extract, Transform, Load) tool simplifies and automates the process of data integration by extracting data from various sources, transforming it into a desired format, and loading it into a target system. The benefits include improved data quality, reduced manual labor, faster data processing, and better decision-making through access to timely and accurate information.

What are the types of ETL Tools?

There are various types of ETL tools, including open-source tools like Apache NiFi and Talend, commercial tools like Informatica and Microsoft SSIS, and cloud-based ETL services like AWS Glue and Google Dataflow. These tools cater to different data integration needs and budgets, offering a range of features and capabilities.

Is SQL an ETL tool?

SQL (Structured Query Language) is not an ETL tool itself. However, SQL can be used as part of ETL processes to manipulate and transform data within a database. ETL tools often incorporate SQL for data transformation and manipulation tasks.

Is AWS an ETL tool?

AWS (Amazon Web Services) is not an ETL tool, but it offers ETL-related services like AWS Glue, which is a fully managed ETL service. AWS Glue helps automate the extraction, transformation, and loading of data from various sources to AWS data storage and analytics services. So, while AWS itself is not an ETL tool, it provides ETL services within its cloud ecosystem.