10 Best Data Integration Platforms in 2025

Data is the foundational building block for modern business. Need to understand where your business, inventory, customer retention, or sales stand? Data. Want to start planning for the future of your business? Data. Ready to adopt AI or other new technologies to help propel your business forward? More than likely, you’ll need data in one form or another to ensure you can effectively use these tools. 

It feels elementary to say, but data is really important. Most people and businesses recognize this. But data is challenging. Chances are if you’re running into problems with your data now, those problems will only compound as you grow your business. Let’s figure out how to make data work for you now so you can build from the best foundation possible. 

The first step to getting your data to a place where you can use it is to get the right data integration tool in place. Data is often messy, difficult to handle manually, and comes from more sources than we often realize. But bringing it all together can give you an unparalleled window into your business. So, let’s figure out how to get it there and make your data usable. 

What is a data integration platform?

Data integration combines data from different sources into a unified location for easier analysis and decision-making. This process involves collecting, transforming, and loading data into a single system for analysis. Because every business has so much data—which is growing exponentially—it’s difficult to scale data integration if your company tries to do it manually. A data integration platform helps extract data, transform it so it can be combined with data from other sources, and load it into a platform or tool so you can analyze and visualize it. 

Once the realm of large companies or tech-heavy industries, data is now a critical tool for organizations of all sizes. Even small businesses now use their data to maintain a competitive edge in supporting and growing their business. Data is only going to become more important for businesses, so having the tools and processes in place to use it effectively will be critical. 

Data integration ensures that all relevant data, regardless of its source, is brought together. This gives your company a comprehensive view of performance that can drive better decision-making. Your integrated data will allow your company to understand trends, find insights about your business, and be more agile in responding to business opportunities efficiently.

Types of data integration platforms

There are different types of data integration. Some tools focus on moving data where you want it to go, others can focus more on combining and transforming data. We’ve defined some general categories for data integration platforms, though some tools can fit into several different categories. These categories are to help you understand what types of data integration are available and what types of features might best suit your business. Here are some common types:

• ETL (extract, transform, load) tools: These data integration tools extract data from multiple sources, transform it to fit operational needs, then load it to a destination system.
• ELT (extract, load, transform) tools: Similar to ETL tools, these platforms are becoming more popular. The difference is that the data transformation doesn’t happen until it gets to its destination system.
• Data migration tools: Designed to move data between different systems, these tools are often useful during system upgrades or consolidations.
• Data preparation tools: These platforms help clean, enrich, and structure data before it is used for analysis, ensuring higher data quality.
• Federated data connections: If your team has already heavily invested in a data storage solution, you don’t want to have to move your data to an additional but redundant data platform. Federated data solutions query data directly from where it’s stored, like a data warehouse or data lake, allowing you to utilize your existing investments in data storage while still benefiting from a business intelligence (BI) tool to combine, visualize, and analyze data. 
• Manual data integration tools: While we don’t recommend manual data integration for all your data needs, sometimes you need to build custom data integrations for specific data sets. These tools have manual coding and scripting when off-the-shelf solutions are not sufficient.

Factors to consider when choosing a data integration platform

As with any tech tool you’re considering purchasing, you need to consider several factors when selecting a data integration platform. By taking the time to think about how you want to use the tool, who will use it, and what your goals are for implementing a data integration platform, you will ensure you find the right tool to meet your needs—one that will actually benefit your business rather than languishing in your tech stack. Here are a few factors to consider: 

Ease of use and user-friendly interface

One of the first questions you can ask is, “Who is going to be using this tool?”. Do you want to have a tool that’s available for a variety of users across your business? Does it need to work for both technical and non-technical employees? 

In the unlikely scenario your business has an IT team in place that is bored and looking for time-consuming and resource demanding projects, then you could probably get a more technical tool that takes a long time to learn and ensures only one person in your business will control who and how you use data. 

Or, you could look for an intuitive, user-friendly tool, ensuring many people on your team get the benefits of data for their specific use cases. This means the tool likely has lots of pre-built connectors, so users only need to put in their login information to begin collecting data. This ease of use opens data up so that everyone in your business has a chance to bring in the data that will affect their jobs and help use data to improve their specific job functions. An intuitive platform reduces the learning curve and increases productivity.

Scalability and performance

Another question you need to ask before choosing a tool is, “What do we want to be able to do with data in the future?” If you don’t know yet, that’s ok. But taking the time to think about it now is a great way to find a tool that will meet your needs now and in the future. This way, you don’t get stuck with a tool that can’t meet your needs as your company grows. 

Right now, you may want to look at data from a few sources—for tasks like analyzing sales data or supply chain costs. You may not need a data integration platform with a lot of advanced or customizable features. A simple tool could easily meet your simple needs. However, if you want to deploy artificial intelligence tools to help streamline business processes in the future, then you’re going to need a lot of data from a lot of data sources that can be transformed and customized to meet your specific needs. In that case, you’re going to need a tool with more advanced features. 

A good data integration platform should be able to handle growing amounts of data and increasing numbers of data sources without compromising performance. It should efficiently manage both small-scale and enterprise-level data integration tasks.

Integration with existing systems and applications

This question might be fairly obvious, but you need to ask it anyway: “Will this tool actually integrate with my current systems?” If you can’t connect the tool to access the data from your current tech stack and legacy applications, then your data is dead in the water and this tool won’t get you very far. 

You need to make sure any data integration platform you choose easily integrates with your current systems and applications. These tools often have pre-built connectors to common applications. They’ll also need features that allow you to build your own connectors to proprietary tools your company relies on. Ensuring compatibility with existing databases, cloud services, and enterprise applications gives you a good start to smooth data flow and can reduce the need for reconfiguration or complex connections that won’t scale easily. 

10 best data integration platforms in 2025

With these key factors in mind, choosing the right data integration platform becomes much easier. The best platform for your business will depend on your specific needs, whether it’s ease of use, expandability, or ease of integration with existing systems.

Ready to get started with your own data integration platform? We’ve put together a list of some of the best options available. Use this guide to identify the tools that best fit your needs then begin choosing the right ones for your business. 

1. Domo

Domo is a cloud-based platform that supports the entire data lifecycle, including comprehensive features for data integration. Domo has thousands of pre-built connectors that can help you bring in popular data sources in just a few minutes. Domo also offers the ability to build custom connectors to proprietary and on-premise solutions. Once data is integrated, Domo provides additional tools and functionalities that allow your team to analyze, visualize, and share the data. One notable feature is Domo’s federated data connectors, which allow your team to access data in your current storage solutions. Federated adapters translate Domo queries to the format used by your existing databases. Domo also includes very strong data governance and security features.

Key Features:

• Real-time data integration
• Extensive library of pre-built connectors
• User-friendly and intuitive data connection tools
• Federated data connectors 
• Once connected, data dynamically and automatically refreshes

2. Fivetran

Fivetran is all about moving your data where you need it to go. As an automated data integration platform, Fivetran has more than 500 pre-built data connectors that simplify the process of centralizing data. It offers reliable and smooth data replication from a wide range of sources to a central data warehouse, ensuring data is consistently up-to-date and ready for analysis. Fivetran also complies with many security certifications, ensuring your data remains protected as you’re moving it where you need it to go. 

Key Features:

• Automated schema migration
• Incremental data updates
• Pre-built connectors for popular data sources
• Strong security features

3. Talend

Talend (owned by Qlik) started as an open-source data integration platform. The open-source version was retired, but Talend now offers a data integration platform with comprehensive ETL capabilities and cloud integration options. As a low-code solution, it is easier for non-technical team members to connect and deploy their own data sources where they need it. Like Domo, Talend is also an end-to-end data management tool, supporting both data integration and analysis. It supports real-time and batch processing, making it suitable for diverse data integration needs.

Key Features:

• Low-code platform 
• Real-time and batch processing
• Built-in data quality tools

4. Matillion

Matillion is a cloud-native ELT platform designed for data transformation in cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake. It offers a user-friendly interface for building complex data pipelines and managing all of your pipelines from one source. Matillion offers no-code and low-code solutions and is focused on tightly integrating AI tools into the data integration process so team members can transform data without extensive technical backgrounds. 

Key Features:

• AI integration to transform data from plain-language queries
• Native integration with cloud data warehouses
• Scalable architecture
• Supports change data capture (CDC) pipelines

5. Informatica

Informatica is a comprehensive data integration platform that supports a wide range of data management tasks, including ETL, data quality, and governance. It was named a leader by Gartner in the data integration space and offers strong and reliable features. Informatica is best known for its features supporting large enterprises but has extensive functionality that can support small and mid-sized businesses, as well. 

Key Features:

• AI-powered data cataloging for better data discovery
• Extensive data governance features
• Strong data management capabilities, including ETL, data quality, and governance

6. DBT (Data Build Tool)

DBT is an open-source data transformation tool for data analysts and engineers to transform data already loaded into a data warehouse. Because it transforms data that has already been stored, you don’t need to invest in additional storage options. It focuses on making data transformations maintainable and easy to implement using SQL. Plus, it’s modular, giving your team flexibility to customize parts of your tool based on the type of data you’re working with.

Key Features:

• SQL-based transformations
• Version control and documentation
• Easily connects with cloud data storage tools like Snowflake, Asure, and Google Cloud

7. Stitch

Stitch is a straightforward data integration platform designed for developers. It simplifies the process of creating data pipelines by providing quick setup and automated data handling from a wide array of sources. And its low-code options mean you don’t have to spend time managing data pipelines; instead, your team can connect data and focus on using that data. It has 140 pre-built connectors (a smaller number than others on this list) but provides low- to no-code features to build custom connectors. 

Key Features:

• Quick setup and configuration
• Supports a wide range of data sources
• Automates handling data schema changes

8. Microsoft Azure Data Factory

If you’ve already invested in Azure for cloud storage, this is an easy choice to implement for data integration. The Azure Data Factory is a cloud-based ETL service from Microsoft that allows for data movement and transformation across various data stores, with native integration to Azure’s cloud storage solutions. It supports both code-free and code-centric ETL approaches to cater to different user needs.

Key Features:

• Code-free and code-centric ETL options
• Integration with Azure services
• Scalable data flow orchestration

9. Oracle Data Integrator

Again, if you’re using Oracle tools already, this is a nice extension to your current tech stack. Oracle Data Integrator (ODI) is tailored for high-performance data integration, especially in large-scale data environments. It uses ELT architecture to optimize data movement and transformation processes and includes native integration with current Oracle databases.

Key Features:

• ELT architecture for better performance
• Integration with Oracle and non-Oracle data sources
• Advanced transformation capabilities

10. IBM InfoSphere DataStage

IBM InfoSphere DataStage is an ETL platform designed for on-premise, enterprise-level data integration, offering capabilities for both big data and real-time processing. It is known for its parallel processing architecture, which enhances performance. Another tool in the IBM portfolio includes IBM App Connect for cloud-storage data integration software. 

Key Features:

• Parallel processing architecture
• Expose data with REST APIs
• Strong data governance features

Feel ready to choose a tool that best meets your needs? We hope that in this list provided you find several possible options to consider. If you have additional questions or you want to talk to someone about the specific needs of your business, please reach out to our product experts at Domo. They can help you better understand how your company can use and deploy a data integration platform.