10 Best Data Integration Tools of 2024: Bridging Data Silos

Data integration—the process of combining data from different sources into a single, unified view—is essential for enterprise work in our data-driven economy. Data integration tools parse through heterogeneous data and extract what’s relevant, transforming vast amounts of structured and unstructured data into actionable information. Finding the right one for your business is critical.

We evaluated the best data integration tools and software on the market to see how they compare on the features most important to enterprise users. Here are our picks for the top data integration tools for teams and businesses in 2024:

• Fivetran: Best tool for an intuitive data integration interface
• Microsoft SQL Server: Best for storing relational databases
• Apache Airflow: Best option for monitoring data workflows
• Informatica PowerCenter: Best for automating ETL testing
• Pentaho: Best data platform for centralizing data intake tasks
• Mulesoft Anypoint Platform: Best software for deploying APIs
• IBM Infosphere Backstage: Best for handling metadata assets
• Talend: Best data integration tool for scaling large datasets
• Boomi: Best software for integrating with enterprise systems
• Oracle Data Integrator: Best for complex data warehouses

Best Data Integration Tool Comparison

Although the data integration tools in this list all offer core data integration capabilities, each has a suite of other key offerings for various use cases. Here’s an at-a-glance comparison of how they stack up in four major categories. Note that all per-user prices are with a one-year commitment, unless otherwise noted.

Fivetran

Best for an intuitive data integration interface

Overall rating: 4.5/5

• Core Features: 4.7/5
• Integrations: 5/5
• Customization: 3.8/5
• Cost: 4.6/5
• Ease of Use: 4.3/5
• Customer Support: 3.3/5

Fivetran is a user-friendly data integration tool that helps you automate extract, load, and transform (ELT) processes with its wide range of prebuilt connectors for various data sources. Users can leverage its intuitive platform to build no-code data pipelines, access a wealth of connectors added by Fivetran’s engineers, or build their own. With little to no scripting required for ELT, users can automate the whole data pipeline even without prior scripting experience.

Pros and Cons

Pricing

• Free version available
• 14-day free trial available
• Contact sales for more information about its four pricing tiers
• See Fivetran’s sample pricing here

Features

• Manage permissions within each resource area of your business or organization to prevent users from accessing irrelevant resources.
• Automatically receive a notification or a message in your dashboard if you encounter a problem with your Fivetran account.
• Generate robust reports, visualizations, and dashboards with prebuilt data models.

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Microsoft SQL Server

Best for storing relational databases

Overall rating: 4.3/5

• Core Features: 4.4/5
• Integrations: 4.4/5
• Customization: 4.3/5
• Cost: 4.5/5
• Ease of Use: 3.6/5
• Customer Support: 4.4/5

Microsoft SQL Server is a relational database management system developed by Microsoft that allows you to organize data points with defined relationships. Users can structure data into tables, views, and indexes that represent predefined categories, making it more flexible to access. Database administrators can also manage physical data storage without affecting the data’s logical structure, so it’s easier to manipulate the data according to your business’s needs.

Pros and Cons

Pricing

• Free to $15,000 per license
• 180-day free trial available

Features

• Take advantage of Microsoft SQL data mining features, which also refer to predictive analytics and machine learning, to easily discover patterns in your data.
• Automatically convert data from one type to another through implicit conversions that aren’t visible to the user and explicit conversions that use CAST or CONVERT functions.
• Users can query and create tables in an external Microsoft database with Microsoft SQL’s built-in data connectors.

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Apache Airflow

Best for scheduling and monitoring data workflows

Overall rating: 4.2/5

• Core Features: 4.5/5
• Integration: 5/5
• Customization: 4.6/5
• Cost: 5/5
• Ease of Use: 3.6/5
• Customer Support: 1/5

Apache Airflow is an open-source platform that helps you schedule and monitor batch-oriented workflows with any technology via its expandable Python framework. Airflow’s scheduler monitors a directed acrylic graph (DAG), which is a collection of all the tasks you want to run. The scheduler also stays in sync with all the DAGs in your directory, collecting parsing results once per minute to check for any active tasks that can be triggered in your workflow.

Pros and Cons

Pricing

• Free to use

Features

• Orchestrate an arbitrary number of workers with Airflow’s modular architecture and message queue feature.
• Write code and generate dynamic data pipelines with the Python framework.
• Monitor your workflows and see a comprehensive view of your ongoing task status and logs via Airflow’s modern web application.

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Informatica PowerCenter

Best for automating ETL testing

Overall rating: 4.1/5

• Core Features: 4.7/5
• Integrations: 4.5/5
• Customization: 4.7/5
• Cost: 2.4/5
• Ease of Use: 3.2/5
• Customer Support: 3/5

Informatica PowerCenter is a robust data integration tool that helps businesses and organizations streamline their extract, transform, and load (ETL) testing processes. Users can automate data integration steps that are often manual and repeated, such as connecting and executing database queries, running jobs, defining variables, and more. You can also set up an automated testing framework to avoid data loss and maintain good data quality for both system testing and regression testing.

Pros and Cons

Pricing

• Contact sales
• 30-day free trial available

Features

• Store information about a wide range of data sources, mappings, workflows, and more with its metadata repository.
• Enables you to define data quality rules with features like data profiling, cleansing, and validation.
• Leverage script-free automation tools for repeatable audit and validation processes.

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Hitachi Vantara: Pentaho

Best for centralizing data integration tasks

Overall rating: 4/5

• Core Features: 4.6/5
• Integrations: 4.2/5
• Customization: 5/5
• Cost: 1.3/5
• Ease of Use: 3.6/5
• Customer Support: 3.8/5

Pentaho Data Integration is a business intelligence (BI) platform that helps businesses organize data better for end users. Deliver comprehensive data analytics to your target audience without the need to write SQL or code in Java or Python. Its intuitive drag-and-drop designer allows you to create data pipelines, while its wealth of prebuilt components helps you prepare and blend data from relational sources, big data stores for on-premises or in the cloud, and more.

Pros and Cons

Pricing

• Contact sales for quote
• 30-day trial available

Features

• Easily create analytics data pipelines with intuitive, drag-and-drop design tool.
• Coordinate and combine data transformations, including notifications and alerts, with Pentaho’s orchestration capabilities.
• Pentaho helps you reduce data prep time and powers predictive intelligence features with advanced analytics models from R, Python, Scala, and Weka.
• Various users, like data analysts, developers, and data stewards, can collaborate in Penthaho’s shared repository.

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Talend

Best for scaling large datasets

Overall rating: 3.9/5

• Core Features: 4.4/5
• Integrations: 4.8/5
• Customization: 5/5
• Cost: 1/5
• Ease of Use: 3.3/5
• Customer Support: 2.4/5

It can be challenging to collect the right data from diverse sources—and an even bigger challenge to do so at the speed your business demands. Talend provides a unified approach that combines rapid data integration, transformation, and mapping with automated quality checks to ensure trustworthy data every step of the way. Integrate virtually any data type from any data source to any data destination, on premises or in the cloud.

Pros and Cons

Pricing

• Offers limited free version (Talend Open Studio)
• Contact sales for quote
• 14-day free trial available

Features

• Build reusable pipelines to extract, improve, and transform data from any source and pass it to any warehouse designation with its self-service pipeline designer.
• Take advantage of Talend’s more than 1,000 connectors and components, which allow you to integrate with any data source and data environment, both in the cloud and on-premises.
• Prepare your data quickly with Talend’s self-service, browser-based, and point-and-click capabilities.

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Mulesoft Anypoint Platform

Best for deploying APIs

Overall rating: 4/5

• Core Features: 4.7/5
• Integrations: 4.7/5
• Customization: 4.1/5
• Cost: 1.3/5
• Ease of Use: 3.6/5
• Customer Support: 2.9/5

MuleSoft’s Anypoint Platform is a data integration solution for developing, deploying, and managing your APIs and integrations. Develop APIs using accessible prebuilt assets found in the MuleSoft ecosystem or by building them via continuous integration/continuous delivery (CI/CD) pipelines. Additionally, users can also transform data automatically, test APIs and integrations, and connect to enterprise applications in their CI/CD pipelines.

Pros and Cons

Pricing

• Contact sales for quote
• 30-day free trial available

Features

• Manage APIs, analyze traffic, monitor service level agreements (SLAs), and fix any integration issues all in a single web interface.
• Leverage MuleSoft’s API designer tool for a visual or a code-based guided experience for designing APIs in multiple languages.
• Secure access points protect your organization’s API by implementing industry-standard encryption and access control via Mulesoft’s API gateway feature.

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IBM Infosphere DataStage

Best for handling metadata assets

Overall rating: 3.9/5

• Core Features: 4.6/5
• Integrations: 4.5/5
• Customization: 4.7/5
• Cost: 1.8/5
• Ease of Use: 2.8/5
• Customer Support: 2.7/5

IBM Infosphere DataStage is a popular data integration tool that enables users to import, export, and manage common metadata assets. Datastage’s metadata asset manager allows you to import assets to a staging area before you share them with your metadata repository. In this repository, you can look for common metadata assets such as implemented data resources, physical and logical data model assets, BI assets, data connections, and custom attributes.

Pros and Cons

Pricing

• Contact sales for quote

Features

• Developers can work more efficiently with DataStage Flow Designer’s machine learning-assisted design tools and an intuitive user interface.
• Cut down on infrastructure management efforts by 65 percent to 85 percent with DataStage’s automated failure detection.
• Set up triggers and actions for continuous integration and CI/CD delivery job pipelines.

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Boomi

Best for integrating with enterprise systems

Overall rating: 3.7/5

• Core Features: 3.7/5
• Integrations: 5/5
• Customization: 3.2/5
• Cost: 2/5
• Ease of Use: 3.6/5
• Customer Support: 4.2/5

Boomi (Dell Boomi) is an integration platform as a service (iPaaS) solution that powers businesses and organizations with intelligent integration of enterprise systems. Boomi’s multi-cloud and independent platform can support the deployment of integrations between different systems like Salesforce, SAP, and Oracle and cloud-based platforms like Amazon Web Services (AWS) and Azure without coding. Users can also leverage its embedded artificial intelligence to help build enterprise-grade security and reliability faster.

Pros and Cons

Pricing

• Contact sales for quote
• 30-day free trial available

Features

• Support the full lifecycle of your APIs in virtually any environment while managing data access for both on-premises and in the cloud setup.
• Boomi’s automated, low-code, cloud-native platform lets users generate more efficient decision-making while ensuring that the data quality is consistent across systems.
• Create reusable components to tailor your applications according to your business needs and goals.

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Oracle Data Integrator

Best for managing complex data warehouses

Overall rating: 3.5/5

• Core Features: 4.2/5
• Integrations: 4.1/5
• Customization: 3.5/5
• Cost: 1.5/5
• Ease of Use: 2.7/5
• Customer Support: 2.8/5

Oracle Data Integrator (ODI) is a data integration platform that provides a fully unified solution for building and deploying complex data warehouses. As ODI relies on a centralized repository, various types of users can access it, including business users and analysts, developers, metadata administrators, and more. Its latest version (ODI 12c) also provides powerful developer productivity and a better user experience with its redesigned flow-based interface.

Pros and Cons

Pricing

• Contact sales for quote
• See the pricing guide for Oracle Integration here.

Features

• Use ODI’s target database features instead of a conventional ETL server to lead and transform data faster and leverage its prebuilt connectors to simplify the integration process with databases and big data.
• Encapsulate flow sections, which can be reused in multiple mappings and also contain sources and targets along with these flows.
• Load multiple targets as a part of a single flow and specify the order of target loading, or choose to use the split component to route flows into different targets.

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5 Key Features of Data Integration Tools

Data integration tools offer standard features for flexibility, interoperability, and scalability, but the top solutions provide businesses with key capabilities for various applications and use cases. To help you find the best data integration tools for your business, look into the features below:

ETL Processes

Extract, transform, and load refers to the three main steps in the data integration process from various sources into a centralized system. Extract includes gathering data from various sources, like databases or APIs; transformation entails converting and cleaning data after extraction to ensure consistency; load refers to the last step where data is loaded into a target system, such as data warehouses, where it can be accessed and analyzed.

Scalability

Scalability refers to a system’s capacity to effectively manage an increasing number of activities and an expanding volume of data. Scalability is essential in ensuring that the data integration solution can support increasing workloads to meet the demands of your business.

Data Warehousing

A data warehouse refers to a type of data management system that provides a centralized repository for cleansing, formatting, and storing data from various sources. It’s also designed to support business intelligence activities such as performing queries and analysis, often involving large amounts of historical data.

Automation & Scheduling

Data teams eliminate manual grunt work and minimize inefficiencies by automating data integration processes such as extracting, transforming, and loading data. Scheduling also allows teams to run tasks at a specified date, time, or interval, increasing efficiency and consistency in the data integration process.

Security & Compliance

Data security in data integration projects can be ensured through various measures, including access controls, encryption, data masking, regular auditing, and data security training. It’s also crucial to comply with data protection regulations such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act of 1996 (HIPAA), Service Organization Control (SOC) Type 2, and more.

How we Evaluated the Data IntegrationTools

We assessed the top data integration solutions based on six main criteria and specific weighted subcriteria. We assigned criteria scores from zero to five, and summed up the total scores to determine the winner for each category as well as the best overall data integration tool. Finally, we assigned a primary use case to each data integration tool included in our list.

Evaluation Criteria

The best data integration tools offer standard features for efficient data handling and a seamless integration process with other software and platforms. We put the most emphasis on the core features and integrations. We then assessed each option’s customization capabilities, followed by cost. Ease of use and customer support solutions wrapped up our evaluation.

Core Features | 35 percent

We looked into each data integration tool’s key offerings including ETL processes, data migration and warehousing, security and compliance, and scalability. Criteria Winner: Informatica PowerCenter, Talend

Integrations | 20 percent

Here, we assessed how each option syncs with other platforms and software, such as MySQL, AWS, Oracle, Microsoft, and APIs. Criteria Winner: Boomi, Apache Airflow

Customization | 15 percent

We considered each data integration tool’s ability to tailor the platform according to a business’s needs, such as data sources, reports, and dashboards. Criteria Winner: Talend, Pentaho

Cost | 10 percent

We assessed the pricing structure in terms of starting license fee cost, free trial length, billing options, and pricing transparency. Criteria Winner: Apache Airflow

Ease of Use | 10 percent

We evaluated how real users across certified review sites perceive the data integration platforms’ usability and learning curve. We also considered each option’s knowledge base and learning resources available. Criteria Winner: Fivetran

Customer Support | 10 percent

We browsed through user reviews and ratings from reliable sites to see how each tool fared in terms of customer support solutions via live chat, phone, and email. Criteria Winner: Microsoft SQL Server

Frequently Asked Questions (FAQs)

What Are the Most Common Types of Data Integration Methodologies?

To facilitate more efficient use of data and the integration process, various data integration methodologies continued to emerge over time, including the following: manual integration, application-based integration, common storage integration, and data virtualization.

• Manual integration refers to the process where data is manually transferred or loaded between systems.
• Application-based integration involves connecting different applications through APIs or middleware.
• Common-stage integration extracts data from sources and stores it in a data warehouse, providing a centralized data source.
• Data virtualization involves taking data from various sources and combining it into a virtual database where end users can easily access it.

How Does Data Integration Work?

Data integration combines data from different sources, such as databases or applications, to create a unified and comprehensive view, which is particularly essential to working with big data. The data integration process offers consistency across various data sources, making it possible for businesses and organizations to analyze combined datasets and find patterns from historical data that were either inaccessible or less commercially viable in the past.

What Are the Three Main Issues Faced in Data Integration?

While there are various issues faced in the data integration process, it’s common for businesses to encounter challenges in data quality, such as ensuring that the combined data is precise, comprehensive, and consistent across multiple sources.

Data compatibility is also one of the main issues in data integration, as handling the disparities between different systems’s formats and structures can make the integration process difficult.

Additionally, businesses might also find data governance challenging due to the strict processes of guaranteeing data security, privacy, and compliance with appropriate rules and standard regulations.

Bottom Line: Best Data Integration Tool

Data integration tools provide businesses with a centralized system to manage giant datasets and bridge data silos across various applications and processes. Although the majority of data integration tools provide capacity for ETL processes, data migration, and governance, each platform varies in usability, customization, and integration ability. Consider the tools and software on our list and evaluate which data integration solution is the best fit for your business.

If you’d like to learn about platforms that offer big data features aside from data integration, read our in-depth review of the best big data analytics tools.