Looking to extract, clean, or synchronize your organization's data?
There are hundreds of ETL pipeline tools out there, but not all are created equal.
We've reviewed the best data integration tools based on their unique features and use cases.
You'll learn about the most important features to look for in a data pipeline tool and how to choose the right connector for your business needs.
Data integration is the process of moving data from one place to another. Sometimes that's from one data source to another, 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 integrations using middleware or common storage.
ETL (extract, transform, load) is a common type of data pipeline that takes data from its original source, cleans and transforms it, and loads it into a warehouse or data lake.
A data integration tool helps you move data from its source to its destination, perhaps performing data transformations between each step.
Data is collected from several apps and loaded into a data warehouse like Snowflake. This single source of truth then feeds into business intelligence tools, analysis software, dashboards, and other platforms to deliver new insights.
Large amounts of data from one or more apps are loaded into the cloud, either in a batch or continuously. The data trains machine learning algorithms using real-time, real-world information.
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 continually update for organizations that plan on using both systems in parallel.
Data from two platforms is collected, cleansed, and synced. A common use case would be synchronizing client information across marketing and sales software, like matching newsletters subscribers in Mailchimp with prospects in Salesforce.
Choosing the right data connector is an important decision. You'll want to look for the following factors:
The best tools have a broad range of pre-built connectors that integrate with the most important data sources and destinations you need. Only choose a tool that lets you access data necessary
Chances are, no single platform will have built-in integrations with all the long-tail sources you need. In that case, consider where you'll get connectors for your missing sources. Will the team develop them? Will you be able to develop them in-house?
Many integration tools use a consumption-based pricing model, which can vary every month. Others charge per data workflow, which provides a consistent price.
If something goes wrong, will you have help when you need it? There's a good chance the APIs your integration depends on will change from time to time. Will the platform handle this maintenance?
You'll also need to decide on a few different options.
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 not offer the most powerful options. A highly experienced team, however, might want more features that a code-only tool offers.
Most modern data sources are in the cloud, so choosing a cloud-based data integration platform makes sense. But if you want more customizability for the integration, an on-premise tool can make sense.
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.
Most data can be handled on a regular schedule, like at the end of each workday. But other data types need to be updated in real-time, like transactions and inventory. Most tools are best at one or the other.
We've reviewed the major data tools, platforms, and vendors. The following are the best data integration tools for this year.
Portable is the best data integration tool for teams with long-tail data sources.
Portable is an ETL/ELT platform that features connectors for 300+ 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.
300+ built-in data connectors.
Fast turnaround time for custom connectors.
Ongoing maintenance of long-tail connectors at no additional cost.
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.
Informatica is a portfolio of high-performance data virtualization tools. It includes features for data governance, integration services, application integration, analytics, and more.
Comprehensive suite of tools including Informatica PowerCenter, Informatica B2B Data Transformation, and more.
Cloud-based and on-premises deployments available.
Advanced data transformation functionality.
Informatica is best for enterprise businesses looking for a robust, comprehensive solution for all kinds of data needs.
Talend is a platform with no-code options for data transformation and ETL. It has a forever free plan, but with limited features and self-service support only.
Comprehensive cloud data integration.
Integration with all major cloud platforms.
Connectors for Hadoop, NoSQL, IoT, machine learning, Spark, and more.
Talend is best for teams that need an all-in-one platform for data connectivity and analysis.
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.
Support for real-time data integration.
Endpoint connectors for public and private clouds.
Designed for speed with an architecture built for faster connections.
Dell Boomi is best for leveraging data across hybrid infrastructures.
Jitterbit is an integration platform-as-a-service (iPaaS) for SaaS and on-premise applications. It uses artificial intelligence technology to automate endpoints.
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.
Jitterbit is best for enterprises that need a single tool for the full data lifecycle.
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.
Support for cloud applications, big data, and IoT integrations.
Real-time data integration capabilities.
Integration with Hadoop and other NoSQL data sources.
SnapLogic is best for non-technical teams that need an integration tool that doesn't require code.
Integrate.io is a data integration platform that supports ETL and ELT workflows. It offers integration services for cloud platforms and on-premise data.
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.
Integrate.io is best for teams who need a powerful platform that doesn't require code.
Oracle has a suite of tools for data integration, including Oracle Data Integrator and Oracle GoldenGate. The platform includes data governance and profiling features.
Fully integrated into the Oracle ecosystem of tools.
Auto-detection of corrupted data and built-in corrective transformations.
Machine learning and AI capabilities.
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.
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.
Supports data replication for unstructured data
Focused on big data applications.
End-to-end analytics reporting data.
Pentaho is best for teams that need a big data tool for on-premise or cloud data integration handled in batches.
Hevo is a data replication and ETL tool for near real-time data processes, including SaaS data sources and on-premise databases.
Support for major data warehouse destinations including Snowflake, AWS Redshift, and Google BigQuery.
Automatic schema detection.
Automated data pipelines with 100+ pre-built connectors.
Hevo is best for teams looking for an automated data pipeline that can automatically detect schema for new data sets.
IRI Voracity is an ETL tool that handles data cleansing for structured, semi-structured, or unstructured formats.
Data validation and enrichment capabilities.
Robust features for governance and data quality.
Features for personally identifiable information (PII) masking and synthetic test data.
IRI Velocity is best for teams that need extensive data cleansing and governance capabilities.
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.
Management of cloud-based and on-premise data.
Complete platform for integration, quality, cleansing, and integration.
Efficient batch processing for big data workflows.
SAP is best for users familiar with the SAP suite of data products.
ZigiOps is a data integration service to streamline data workflows across different types of data. It offers no-code features and real-time data integration.
Smart data loss prevention.
Works for enterprise data in the cloud and on-premise.
Functionality for deep integrations, mapping, and filtering.
ZigiOps is best for teams looking to automate as many data integration services as possible.
Microsoft has several data services in its suite of tools, including Azure Logic Apps, Microsoft Flow, and SQL Server Integration Services (SSIS).
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.
Microsoft's data integration tools are best for companies with deep integrations with Azure or other tools in the Microsoft ecosystem.
IBM has several data tools, including InfoSphere DataStage and App Connect.
Massively parallel processing capabilities.
Robust data quality features, including profiling, matching, enrichment, and standardization.
Support for cloud-based and on-premise data sources.
IBM's suite of data integration tools is best for teams already using IBM tools in other areas.
Choosing the right data integration tool can help your business grow, or it can slow you down. There are plenty of options on the market, and each has its strengths and weaknesses.
Depending on 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.
Most importantly, choose a tool that integrates with the data sources you need and works with your current systems. Portable handles all development, maintenance, and troubleshooting for you, so your team can focus on using your data---not moving it around.