Best 15+ Data Integration Tools & Solutions (October 2024)

As companies become much more data-driven, they'll need to integrate different sources together to glean meaningful insights. This shift means that they'll need to extract, clean, and automate data synchronization processes.

There are dozens of ETL data platforms, but not all are created equal.

We've reviewed the best data integration solutions based on their unique features and use cases.

This guide covers the top features to look for in a data pipeline tool and how to choose a suitable data connector for your business needs.

What is Data Integration?

Data integration is the process of moving data from one place to another. Sometimes that's between different sources, like keeping the information in your CRM and ERP tools in sync. Other times that means storing big data in a data warehouse for later use.

There are several types of data integration. These include ETL, manual data integration, and middleware or standard storage integrations.

Extract, transform, load (ETL) is a common type of data pipeline that takes data from its source, cleans and transforms it, and loads it into a warehouse or data lake.

Typical data integration functions

Ingesting raw data formats to move data from a legacy system into modern data warehouse
Data orchestration between data ecosystems like Amazon or Microsoft with critical business apps.
Enforcing data management principles to ensure stored data is secure and accessible to authorized users.

Data integration tools helps you move data sets from its source to its destination, performing data transformations and additional workflows between each step.

Data Integration Use Cases:

1. Business intelligence

2. Machine learning

3. Legacy system data migration

4. Software sync

Business Intelligence (BI)

Data is collected from several apps and loaded into a data warehouse.
This single source of truth feeds into business intelligence tools, analysis software, dashboards, and other platforms to deliver new insights.

Machine Learning

Large amounts of data from one or more apps are loaded into the cloud, either in a batch or continuously.
Machine learning algorithms use real-time, real-world content fed by various APIs.

Legacy System Data Migration

Many organizations still rely on legacy on-premise systems but want to begin the transition to modern cloud-based web services.
Data integration tools can ensure a smooth transition or continual update for organizations that plan on using both systems in parallel.

Software Sync

The synchronization between two different sources involves collecting, storing, sanitizing, and preparing the data for transmission.
An everyday use case would be marketing and sales software, like matching newsletter subscribers in Mailchimp with prospects in Salesforce.
Manually, this is labor intensive, but when you automate it, you achieve greater scalability.

We've reviewed the major data tools, platforms, and vendors. The following are the best data integration tools for this year.

Top 15 Data Integration Tools

We've reviewed the major data tools, platforms, and vendors. The following are the best data integration tools for this year.

Portable
Informatica
Talend
Dell Boomi
Jitterbit
SnapLogic
Integrate.io
Oracle
Pentaho
Hevo
IRI Voracity
SAP
ZigiOps
Microsoft
IBM

1. Portable

Portable is the best data integration tool for teams with long-tail data sources.

Portable is a no-code ETL/ELT platform with connectors for 450+ hard-to-find data sources.

The Portable team will develop and maintain custom connectors on request, with turnaround times as fast as a few hours.

Key features

450+ built-in connectors for numerous data services.
Fast turnaround time for custom connectors.
Ongoing maintenance of long-tail connectors at no additional cost.

Best suited for

Portable is best for teams that need to connect several data sources and want to focus on gleaning insights from data instead of developing and maintaining data pipelines.

2. Informatica

Informatica is a portfolio of high-performance data virtualization tools. It includes features for data governance, integration services, application integration, analytics, and more.

Key features

A comprehensive suite of tools, including Informatica PowerCenter, Informatica B2B Data Transformation, and more.
Cloud-based and on-premises deployments are available.
Advanced data transformation functionality.

Best suited for

Informatica is best for enterprise businesses looking for a robust, comprehensive solution for all kinds of data needs.

3. Talend

Talend is a platform with no-code options for data transformation and ETL. It has a forever free plan but limited features and self-service support only.

Key features

Comprehensive cloud data integration.
Integration with all major cloud platforms.
Connectors for Hadoop, NoSQL, IoT, machine learning, Spark, and more.

Best suited for

Talend is best for teams that need an all-in-one data connectivity and analysis platform.

4. Dell Boomi

Dell Boomi is a low-code tool that works on public clouds, private clouds, and on-premise deployments. It uses a graphical user interface, automation, and a unified reporting portal.

Key features

Support for real-time data integration.
Endpoint connectors for public and private clouds.
Designed for speed with an architecture built for faster connections.

Best suited for

Dell Boomi is best for leveraging data across hybrid infrastructures.

5. Jitterbit

Jitterbit is an integration platform-as-a-service (iPaaS) for SaaS and on-premise applications. It uses artificial intelligence technology to automate endpoints.

Key features

Works for cloud-based and on-premise data flows.
AI engine for a more efficient data integration process.
Automapper feature with 300 prebuilt templates to speed up the data transformation process.

Best suited for

Jitterbit is best for enterprises that need a single tool for the entire data lifecycle.

For distributors and eCommerce companies, we recommend DCKAP as a top data integration solution rather than Jitterbit.

6. SnapLogic

SnapLogic is a data integration solution that uses artificial intelligence to create automations. It uses a drag-and-drop visual platform that doesn't require code.

Key features

Support for cloud applications, big data, and IoT integrations.
Real-time data integration capabilities.
Integration with Hadoop and other NoSQL data sources.

Best suited for

SnapLogic is best for non-technical teams that need an integration tool that doesn't require code.

7. Integrate

Integrate.io is a data integration platform that supports ETL and ELT workflows. It offers integration services for cloud platforms and on-premise data.

Key features

Offers for no-code and low-code cloud data integration.
Universal REST API connector for easier data ingestion.
Supports transformations between internal databases and cloud warehouses.

Best suited for

Integrate.io is best for teams who need a powerful platform that doesn't require code.

8. Oracle

Oracle has a suite of tools for data integration, including Oracle Data Integrator and Oracle GoldenGate. The platform includes data governance and profiling features.

Key features

Fully integrated into the Oracle ecosystem of tools.
Auto-detection of corrupted data and built-in corrective transformations.
Machine learning and AI capabilities.
Metadata extraction.

Best suited for

Oracle is one of the most cost-effective solutions for enterprises that need a data integration solution for massive amounts of data. It's also the easiest solution for businesses fully integrated into the Oracle ecosystem.

9. Pentaho

Pentaho is a platform now owned by Hitachi Vantara that provides a range of tools for data operations. It's focused on batch processing for data management and analytics.

Key features

Supports data replication for unstructured data
Focused on big data applications.
End-to-end analytics reporting data.

Best suited for

Pentaho is best for teams that need a big data tool for on-premise or cloud data integration handled in batches.

10. Hevo

Hevo is a data replication and ETL tool for near real-time data processes, including SaaS data sources and on-premise databases.

Key features

Support for data warehouse destinations, including Snowflake, AWS Redshift, and Google BigQuery.
Automatic schema detection.
Automated data pipelines with 100+ pre-built connectors.

Best suited for

Hevo is best for teams looking for an automated data pipeline that automatically detects schema for new data sets.

11. IRI Voracity

IRI Voracity is an ETL tool that handles data cleansing for structured, semi-structured, or unstructured formats.

Key features

Data validation and enrichment capabilities.
Robust features for governance and data quality.
Features for personally identifiable information (PII) masking and synthetic test data.

Best suited for

IRI Velocity is best for teams that need extensive data cleansing and governance capabilities.

12. SAP

SAP is a suite of tools, including SAP Data Integrator, SAP Cloud Platform, and more. It integrates with other SAP products, like its flagship ERP platform.

Key features

Management of cloud-based and on-premise data.
A complete platform for integration, data quality, cleansing, and integration.
Efficient batch processing for big data workflows.

Best suited for

SAP is best for users familiar with the SAP suite of data products.

13. ZigiOps

ZigiOps is a data integration service to streamline workflows across different data types. It offers no-code features and real-time data integration.

Key features

Smart data loss prevention.
Works for enterprise data in the cloud and on-premise.
Functionality for deep integrations, mapping, and filtering.

Best suited for

ZigiOps is best for teams looking to automate as many data integration services as possible.

14. Microsoft

Microsoft has several data services in its suite of tools, including Azure Logic Apps, Microsoft Flow, and SQL Server Integration Services (SSIS).

Key features

Built-in support for Microsoft SQL Server and Azure Data Factory.
Scalable, pay-as-you-go service.
Easy data mapping without a steep learning curve.

Best suited for

Microsoft's data integration tools are best for companies with deep integrations with Azure or others in the Microsoft ecosystem.

15. IBM

IBM has several data tools, including InfoSphere DataStage and App Connect. IBM is regarded as one of the pioneers of data management and mainframes of the past.

Key features

IBM provides massively parallel processing capabilities.
Robust data quality features, including profiling, matching, enrichment, and standardization.
IBM supports cloud-based and on-premise data sources.

Best suited for

IBM's suite of data integration tools is best for teams already using IBM tools in other areas.

How to choose a Data Integration Solution

Choosing the best data integration platform is an important decision. You'll want to look for the following factors:

Data sources

The best tools have a broad range of pre-built connectors that integrate with the most critical data sources and destinations you need. Only choose a tool that lets you access the data necessary. Most of these data sources operate using safe and secure APIs.

Extensibility

No single platform will have built-in integrations with all the long-tail sources you need. In that case, think about where you'll get connectors for your missing sources. Will the team develop them? Can you create them in-house? Open-source documentation helps, but personalized support is better.

Pricing model

Many integration tools use a consumption-based pricing model, which varies every month. Others charge per data workflow, which provides a consistent price. Ensure you have a good idea of each platform's scalability costs.

Support

If something goes wrong, will you have help when you need it? The APIs your integration depends on will change from time to time. Will the platform handle this maintenance? Some data integration tools require you to be on the most expensive plan to get assistance.

Ease of use vs. customizability

Most integration tools are either user-friendly or highly customizable. For non-technical teams, a no-code platform might be the best choice, even though it might offer less powerful options. A highly experienced team; however, might want more features that a code-only tool provides.

On-premise vs. cloud-based

Most modern data sources are in the cloud, so choosing a cloud-based data integration platform makes sense. But an on-premise tool can make sense if you want more customizability for the integration. Look for free open-source ETL tools to load on-premises data to the cloud.

Proprietary vs. open-source

Most SaaS data integration tools are proprietary. They require less maintenance but cost money each month, and you won't be able to adapt the software. Open-source tools let you make changes and are usually free but require you to manage all overhead like servers, updates, security, and more. With ample support and seamless integration with data warehouses, you most likely don't need your data integration platform to be open-source.

Batch vs. real-time processing

Data integration tools can handle most data regularly, like at the end of each workday. But other data types, like transactions and inventory, need to be updated in real-time. Understand the costs and functionality for automated data processing and manual data syncs.

Automate Syncs With a Data Integration Solution

The correct data integration tool helps your business grow and maximizes your team's resources. There are plenty of ETL tools on the market, each with strengths and weaknesses.

Whether you're looking to warehouse big data for machine learning or business analytics, synchronize separate systems, or something else, there's a data solution tailored to your needs at the right price.

Above all, choose a tool that integrates with the data sources you need and works with your current systems. Portable handles the data orchestration for you, so your team can leverage the data — not scouring API docs. Try it free today!