The world of data is constantly evolving and advancing, bringing exciting new opportunities for those who wrangle it. To make the most of your critical data, you must first map it. However, an increasing number of sources and formats results in a tricky mapping process.

Data mapping tools can simplify this process, ensuring you can visualize, analyze, and interpret data accurately and efficiently.

Here are five key takeaways to note about data mapping tools:

  • When looking for data mapping tools, must-have features include visual data mapping, pre-built templates, data validation, transformation functions, and more.
  • Data mapping can be complex. The data mapping tool you choose should be easy to use and scalable to fit your organization's needs.
  • Your data mapping tool should also feature integration capabilities so it works with your current tech stack.
  • Various robust data mapping tools exist on the market today, including, Talend, Informatica, and Jitterbit.
  • Key differences between the top data mapping tools involve pricing, customer support, and usability.

In this guide, we'll dive into the top data mapping tools that are sure to benefit your business. We'll analyze their key features, pricing, and more to help you pick the right one for you.

Table of Contents

What Is Data Mapping?

Data mapping is a key process that connects data elements from one format or structure with their corresponding elements in another format or structure.

It plays an essential role in moving data between systems while ensuring accuracy and consistency throughout the transfer. Data mapping plays a crucial role in the data integration, transformation, and migration processes.

To perform data mapping, you must first identify how the data elements in the source and target systems relate to each other, and then create rules to convert or modify the data as necessary. This can be done using different methods, such as manual mapping, specialized software tools, or custom scripts. 

The complexity of the process depends on how similar the data structures are and the extent of data transformation needed. In some cases, extra steps like data validation, transformation, or aggregation may be required.

Data mapping is used by a wide range of professionals and organizations, including:

  • Data engineers and data architects: These professionals are responsible for designing and implementing data integration and migration processes, and they use data mapping to ensure the proper flow of data between systems.
  • Data analysts and business analysts: They often use data mapping to transform and integrate data from various sources, enabling them to perform advanced analysis and generate insights that support decision-making.
  • Database administrators (DBAs): DBAs are responsible for maintaining database integrity, performance, and security. They often use data mapping when migrating or consolidating databases or integrating new data sources into existing systems.
  • IT professionals and system integrators: They use data mapping to integrate data from disparate systems during software implementation or upgrade projects, ensuring seamless data flow and minimizing the potential for data loss or corruption.
  • ETL (Extract, Transform, Load) experts: These professionals specialize in moving data between systems, and they rely on data mapping to guide the transformation and loading processes.

The Importance of Data Mapping Tools

Data mapping tools play a crucial role in data analytics by streamlining the process of connecting data elements from various sources and formats and transforming them into a unified, consistent format.

These tools are essential for organizations looking to harness the power of their data, as they enable more accurate and efficient analysis, leading to improved decision-making.

The importance of data mapping tools can be highlighted in several key areas:

  • Accuracy and consistency: Using a data mapping tool helps ensure data is accurately matched and consistently transformed, reducing the risk of errors during analysis. This then leads to more informed decision-making.
  • Time and resource efficiency: Data mapping tools automate most of the mapping process, reducing the need for manual intervention, resulting in saved time and resources.
  • Scalability: As organizations grow and their data sources increase in complexity, data mapping tools become even more vital, as they can handle large volumes of data from various sources and formats.
  • Data integration: Data mapping tools are pivotal in integrating data from disparate sources, allowing organizations to create a comprehensive view of their data. By combining and harmonizing data from multiple systems, organizations can gain deeper insights and uncover hidden patterns or trends that would otherwise go unnoticed.

Key Features of Data Mapping Tools

Data mapping tools have various features that make them invaluable for handling data integration, transformation, and migration tasks. Look for these key features when considering a data mapping tool:

  • Intuitive interface: A user-friendly interface allows for easy navigation and understanding of the data mapping process, enabling users with varying levels of technical expertise to work efficiently.
  • Visual data mapping: A graphical representation of the data mapping process enables users to visually connect the source and target data elements. As a result, users can understand and manage the relationships between different data sets.
  • Pre-built templates: A collection of pre-built templates helps streamline common mapping tasks, reducing the time required to create new mappings and increasing productivity.
  • Transformation functions: Built-in data transformation functions allow users to manipulate, cleanse, and transform data as needed.
  • Data validation: This enables users to validate data during mapping, ensuring it meets specific quality requirements or business rules.
  • Support for multiple data formats: The data mapping tool you choose must be compatible with a wide range of data formats and sources, such as databases, flat files, XML, JSON, and APIs.
  • Automated mapping: This functionality enables the automatic generation of mappings based on matching criteria, thus speeding up the mapping process and reducing manual effort.
  • Version control and collaboration: These tools allow multiple users to work on the same data project simultaneously.
  • Error handling and reporting: These features help users to identify, log, and report errors during data mapping.
  • Performance and scalability: Efficient handling of large data sets and complex mapping scenarios is important for ensuring the tool can scale up to meet the demands of growing organizations.

10 Top Data Mapping Tools

We have carefully selected these 10 data mapping tools based on their features, ease of use, and overall effectiveness in handling diverse data mapping tasks.

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Rating: 4.3/5 (G2)

Key Features

  • No-code/low-code UI:'s drag-and-drop interface simplifies the data mapping process even for users without technical knowledge or coding experience.
  • Hundreds of rare and popular connectors:'s pre-built connectors simplify the integration of diverse data sources and destinations.
  • Scalability and performance: The tool ensures efficient and reliable data mapping by automatically scaling its cloud-based infrastructure to accommodate growing data needs. 
  • Out-of-the-box data transformations: offers a robust set of built-in data transformation functions, allowing users to effectively manipulate, cleanse, and prepare their data during mapping.
  • Security and compliance: prioritizes data security with features such as data encryption at rest and in transit, access controls, and audit logs. These security measures and the platform's GDPR, HIPAA, and SOC 2 compliance ensure that data mapping processes are protected and adhere to industry standards and regulations.
  • Data observability: Monitoring and automated alerts give users more control over their data. Alert types include nulls, cardinality, variance, and more. is a cloud-based, no-code data mapping and ETL platform designed to help businesses efficiently create and manage clean, secure data pipelines. Its drag-and-drop interface allows users with no coding experience to develop and manage complex workflows.

The platform offers hundreds of pre-built connectors, enabling seamless data mapping between various sources and destinations. This extensive connectivity ensures you can map data across systems and formats in a fraction of the time required by manual coding.

Moreover, features blazing-fast ELT data replication capabilities, unifying data every 60 seconds to provide a unique and reliable source for reporting.’s flexible pricing plans make it a great choice for any organization, regardless of how much data it generates. Pricing starts at $199 a month (or $159 if you pay annually) for the ELT & CDC components, for which you will enjoy unlimited connectors, VPN tunnels, and a free initial sync.

In addition, there are three plans available for the ETL and Reverse ETL components, with pricing starting at $15,000 per year for the Starter plan. This includes unlimited packages, transfers, and users, plus a tailored 30-day onboarding process and 24/7 support.

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2. Talend

Rating: 4.0/5 (G2)

Key Features

  • Data profiling: Talend's data profiling capabilities help organizations identify data quality issues, ensuring the accuracy and consistency of data mappings.
  • Specialized connectors: Talend offers specialized connectors for moving large volumes of data, enabling efficient data mapping in high-scale environments.
  • Spark batch processing: Talend leverages Spark batch processing for high-end data movement, ensuring efficient and reliable data mapping across large datasets.

Talend is a comprehensive data integration platform offering a wide range of services such as ESB, iPaaS, and ETL. It aims to provide an all-in-one solution for connecting apps, integrating data warehouses, managing ETL pipelines, and fueling business intelligence.

The platform supports various deployment options, including multi-cloud, hybrid-cloud, and on-premises, offering the flexibility of choosing the best approach for your specific needs.

In addition to the numerous pre-built connectors for popular data sources and destinations, Talend also supports application-to-application integration. This feature is most commonly used by large organizations looking to streamline data mapping between different applications within its technology stack.

Talend currently does not disclose its pricing on its website. For a custom quote, you'll need to contact their sales department.

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3. Informatica

Rating: 4.2/5 (G2)

Key Features

  • Automated session scheduling: Informatica allows users to schedule data integration tasks, ensuring that data mappings are regularly updated and maintained.
  • In-depth monitoring console: Informatica's monitoring console provides detailed insights into data pipelines and mapping processes, enabling users to quickly identify and address any issues that may arise.
  • Custom code support: Users can extend the platform's basic functions by writing custom code in Java or JavaScript, offering additional flexibility in the data mapping process.

Informatica is a comprehensive data integration cloud platform that enables organizations to manage data mapping. Its drag-and-drop interface simplifies the process of creating complex data pipelines, while its modern architecture provides optimal performance and resiliency.

What’s unique about Informatica is that it offers a metadata-driven, AI-powered technology capable of automating thousands of manual tasks related to data management.

You can try Informatica for free, then opt for a pay-as-you-go option with pricing depending on usage.

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4. Jitterbit

Rating: 4.6/5 (G2)

Key Features

  • Pre-built integrations: Jitterbit offers reusable, customizable, and easy-to-implement pre-built integrations that accelerate the automation of common business processes, simplifying data mapping tasks.
  • Management Console: Jitterbit's Management Console offers a centralized view for controlling and monitoring workflow integrations and processes, enabling organizations to manage data mappings effectively across the enterprise.
  • Security and compliance: Jitterbit ensures secure integration processes by complying with GDPR, ISO 27001, SOC 1 and SOC 2, and HIPAA standards.

Jitterbit is an iPaaS platform that helps organizations manage data mappings through its Harmony solution. It allows you to integrate data across various systems, including cloud, on-premises, and legacy applications.

Users can easily create automated workflows for data mapping with its low-code graphical interface and pre-built connectors. This makes Jitterbit a great choice even for non-technical people.

In addition, the platform’s API Manager functionality allows organizations to create, publish, and manage APIs throughout their lifecycle, further facilitating data mapping between various applications and systems.

Jitterbit offers three pricing plans but doesn’t disclose exact costs. You’ll have to get in touch with their sales department to get a custom quote.

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5. Dell Boomi AtomSphere

Rating: 4.3/5 (G2)

Key Features

  • Pre-built connectors: Boomi offers a wide range of pre-built connectors that eliminate the time-consuming task of manual coding.
  • Self-managed: Boomi automatically updates and deploys synchronized changes to your integration processes, allowing you to focus on innovation rather than maintenance.
  • Crowdsourced troubleshooting: Boomi provides common error resolutions within the user interface based on crowdsourced contributions from the Boomi support team and user community

Dell Boomi AtomSphere is a cloud-based integration platform that can help organizations streamline data mapping. It offers pre-defined accelerators, recipes, and solutions that simplify data mapping and ensure a smooth workflow between various applications and data sources.

The platform’s Flow feature allows users to design and automate next-generation workflows and cloud applications quickly using low-code technology. This makes data mapping more efficient and accessible to users with varying technical expertise.

Dell Boomi AtomSphere offers a free trial, so you can test it before deciding if it's right for your business. You must contact their sales department for a custom quote if you want to continue using it.

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6. MuleSoft Anypoint

Rating: 4.4/5 (G2)

Key Features

  • Flexible: MuleSoft Anypoint can be deployed in any architecture or environment.
  • Advanced security: MuleSoft Anypoint offers automated and consistent security for APIs and data as it is compliant with ISO 27001, SOC 2, PCI DSS, and GDPR.
  • Comprehensive API management capabilities: MuleSoft helps businesses manage, secure, and scale all APIs from a single place, onboard developers with precision, and make smarter API program decisions with contextual analytics.

MuleSoft Anypoint is a powerful tool for APIs and integrations that helps organizations manage data mappings efficiently. It has data transformation capabilities that simplify data mapping between diverse protocols and formats while integrating various applications, systems, and services.

The platform’s real-time observation of APIs and integrations helps organizations monitor and diagnose data mapping operations to quickly identify and resolve any possible issues.

MuleSoft Anypoint pricing plans are based on features. They do not actively disclose their pricing, so you must contact them for a custom quote. However, you can try the platform by signing up for a free trial.

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7. Pentaho

Rating: 4.3/5 (G2)

Key Features

  • Accelerated data onboarding: Pentaho offers broad connectivity to virtually any data source or application, providing an easy drag-and-drop interface for creating data pipelines and templates. 
  • Flexible data self-service: Pentaho features a powerful transformation engine with high-performance capabilities, allowing users to visualize, blend, and connect to data anywhere.
  • Metadata injection: Pentaho's metadata injection feature accelerates complex onboarding projects by reusing the same transformation for multiple projects. This promotes efficiency and consistency across data mappings.

Pentaho, a Hitachi Vantara product, is a comprehensive data integration and business analytics platform. Its drag-and-drop interface allows users to create data pipelines that transform and clean data as it moves from one system to another, ensuring it’s analytics-ready and adheres to the required structure and format. 

The platform’s robust dataflow orchestration capabilities and reusable templates for data mapping help businesses manage and coordinate advanced data processing tasks. This reduces the time and effort needed to build and maintain data pipelines.

Plus, Pentaho can combine data from both cloud-based and on-premise sources. Pentaho’s pricing is based on usage and is unique for each organization, so you must contact them to get a custom quote.

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8. Altova MapForce

Rating: 4.5/5 (G2)

Key Features

  • Database key settings: MapForce allows users to customize the target database's primary and foreign key values by either providing the values from within the tool or by letting the database system handle auto-value generation.
  • Define database table actions: MapForce offers the ability to select database table actions to control how data is written to the database, a great feature for automating advanced data management tasks.
  • Support for SQL stored procedures: MapForce provides robust support for stored procedures as input components (procedures that yield results) or output components (procedures that insert or update data). 

Altova MapForce is a powerful and flexible data mapping tool. It allows for any-to-any conversion and integration, providing users with a graphical interface to transform data and automate recurrent processes. 

The platform’s stored procedures can be inserted as function-like calls, enabling users to input data, execute the stored procedure, and map the output data to other components.

In addition, Altova MapForce allows users to define columns within a table to determine which action should be executed in the database. This allows for efficient manipulation of database rows in response to various data formats, such as XML, JSON, Excel, and more.

MapForce comes in three editions, with pricing dependent on functionalities. The Basic Edition pricing starts at $299.00 and provides support for XML-to-XML data mapping and XSLT 1.0/2.0/3.0 code generation.

The more comprehensive Enterprise Edition provides access to all MapForce’s features, with pricing starting at $999.00.

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9. IBM InfoSphere

Rating: 4.0/5 (G2)

Key Features

  • Better visibility: InfoSphere improves information governance by providing complete, authoritative views of information with proof of lineage and quality. 
  • Robust data integration capabilities: InfoSphere supports various data delivery methods, such as bulk data delivery (ETL), virtual data delivery, or incremental data delivery (CDC).
  • Flexible and scalable: InfoSphere can handle all phases of an effective data integration project, adapting to the evolving lifecycle of your project.

IBM InfoSphere is a comprehensive data integration and data management platform. It offers a modern and consolidated user interface, improved runtime and management of data lakes, and enhanced connectivity. 

The InfoSphere Information Server suite includes numerous components, each providing distinct capabilities for data integration. When combined, these components form the building blocks necessary to deliver trusted information across your enterprise, regardless of the complexity of your environment. 

IBM InfoSphere is available for deployment on-premises and in the cloud. The IBM Cloud-Managed plan starts at $19,000 per month.

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10. Astera

Rating: 4.3/5 (G2)

Key Features

  • Drag-and-drop design: Astera eliminates the need for coding by offering an intuitive drag-and-drop interface that simplifies data management.
  • Support for various unstructured formats: Astera offers support for various unstructured and semi-structured data formats such as PDF, TXT, JSON, PRN, RTF, XLX, XLSX, and COBOL.
  • Parallel processing: Astera's server enables running concurrent data extraction jobs, minimizing time and resources used while condensing all data-related tasks into a single, easy-to-use platform.

Astera is a data integration and management platform that simplifies data mapping processes, allowing organizations to effectively manage and extract value from unstructured and semi-structured data formats. 

The platform’s ReportMiner component enables businesses to create pattern-based extraction templates using the no-code, drag-and-drop user interface. These templates can be reused for any file that matches the template, streamlining data extraction and reducing manual effort.

Moreover,  Astera allows users to create custom data quality rules and validate extracted information, thus improving the quality and reliability of the extracted data.

Astera’s pricing depends on the components you plan to use, so you must contact their sales department for a custom quote.

Map Your Data With Ease With is a powerful data integration platform designed to simplify data mapping and transformation tasks. It offers a user-friendly interface, extensive connectivity options, and robust data processing capabilities, making it an ideal choice for businesses looking to streamline their data workflows.

Sign up for a personalized demo and see how can help you automate the data mapping process.

Data Mapping FAQs

What Is Data Mapping Used For?

The data mapping process transforms and transfers data between different systems, databases, or formats. It's a critical part of data integration, migration, and transformation projects, where data from various sources must be harmonized and consolidated to ensure consistency, accuracy, and usability.

What Are Examples of Data Mapping?

Data mapping is used for converting data formats, for instance, transforming XML data to JSON. Moreover, data mapping is important in applying data quality rules and transformations during an ETL (Extract, Transform, Load) process, which helps maintain data accuracy and consistency across different systems.

What Is the Best Data Mapping Tool?

The best data mapping tool is one that fits your specific needs and requirements, which means you might have to try a few before you find the perfect fit. is a popular choice for many organizations because of its robust data integration and transformation capabilities. Its intuitive, no-code/low-code interface makes it easy for anyone to set up complex data pipelines with advanced data mapping capabilities in a matter of minutes.