14 Top Data Mapping Tools for 2024

For analytics use cases, your data would eventually move from your data warehouse to your BI tools.

Hence another iteration of data mapping would be needed to fine-tune your data to extract the most insightful information and adhere to the intricacies of the tool and the analytics process. The same data may need to be pre-processed differently, based on the target of your different analytic processes.

Therefore, a powerful and versatile mapping tools are crucial to your data usage strategy. This article will help you with the selection of the best data mapping tools by providing all details required for selection of this crucial tool for you business.

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Importance of Data Mapping in ETL Process

To leverage data and extract business value from it, the information is collected from several external and internal sources. This information must then be unified and transformed into a format that is suitable for the operational and analytical processes, achieved through Source to Target Mapping.

• Automated Data Mapping & Data Integration: To successfully integrate data the Data Model for the source and target data repositories must be the same. Since this is rare in a Data Warehouse, Data Mapping Software bridges the differences in the schemas of the source and target repositories. This will allow businesses to consolidate important information from different data points with ease.
• Data Mapping Assists in Data Migration: Inaccurate and invalid Data Mapping during the Data Migration phase impacts the accuracy and completeness of the data being migrated. So a code-free mapping solution that can automate this process is essential for Data Migration.
• Automated Data Mapping & Transformation: Data Transformation is essential when it comes to drawing insights and breaking information silos since enterprise information is present in several locations and formats. Data Modeling is the first step in Data Transformation which helps create a framework of what changes are to be made to data before it is loaded to the target database using the Data Conversion Mapping feature offered by Data Mapping Tools.

Key Features Required for Data Mapping Tools

To ensure the success of any Data Integration, Enterprise Data Transformation, or Data Warehousing project, you must select the right Data Mapping Software that fits your needs perfectly. To commence the process of selecting the right tool, you first need to identify the unique Data Modeling requirements and must-have features.

So, some of the key features that a good Data Mapping solution must include are:

• Graphical Drag-and-Drop, Code-Free User Interface: It is imperative to pick a Data Mapping Tool that offers a code-free way to create data maps and process data using the largely intuitive drag-and-drop user interface.
• Ability to Schedule & Automate Database Mapping Jobs: It is essential to pick a Data Mapping Software that can organize the Database Workflow by leveraging a time-based mapping function and event schedule that is triggered by specific events.
• Instant Data Mapping Preview: Choose a Data Mapping Software that can prevent application mapping errors at the design time. This would allow the user to view the processed and the raw data at any step of the Data Modeling process.
• Support for Diverse Systems: All the Data Mapping Software should provide connectivity to a wide array of structured, unstructured, and semi-structured data sources which includes databases, REST APIs, and web services along with FLAT file formats, like XML, JSON, EDI, Excel, etc.

Best Data Mapping Tools

1. Hevo Data
2. Pentaho Data Integration
3. CloverETL (CloverDX)
4. Pimcore
5. Informatica PowerCenter
6. IBM InfoSphere
7. Microsoft SQL Integration Services
8. WebMethods
9. Oracle Integration Cloud Service
10. Dell Boomi AtomSpehre
11. Talend Cloud Integration
12. Jitterbit
13. MuleSoft Anypoint Platform
14. SnapLogic

1. Hevo Data

Rating:4.4/5 (G2)

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your requirements. The platform has the capability for integration with 150+ Data Sources (40+ free sources). And, contains features for auto schema mapping along with many others.

• Automated schema mapper feature: This allows you to automate creating and managing all the mappings and also highlights any incompatible incoming Events as Failed for your review. You can also manually map an incoming event type to a destination table which can be mapped to a different Destination table later.
  • Apart from this any fields containing sensitive data can be skipped. You can also flatten nested objects into a de-normalized Destination table if you want to improve the query performance and avoid unwanted complexities in schema.
• Data transformation: You can modify and enrich your in-flight data using drag and drop or python console.
• Incremental data load: This allows updating data that is modified in real-time and an effective use of bandwidth on source and destination.

2. Pentaho Data Integration

Rating:4.4/5 (G2)

Pentaho Data Integration (PDI) is an open-source Data Integration tool by Hitachi Data Systems. It offers ETL solutions to enterprises that need automatic Data Mapping and loading of data from source to destination.

The provides solutions for Data Minning, Data Warehousing, and Data Analysis. The other services it provides are OLAP services, reporting, information dashboards, and Data Mining. Pentaho Data Integration tool is codenamed Kettle.

Key Features

• The tool offers a No-code GUI interface for users to effortlessly map data from source to destination and save time.
• It supports deployment on single-node computers as well as on a Cloud, or cluster.
• Efficient transformation engine: It has a flexible high-performance abilities that help to visualize, combine, and connect to data wherever required.
• Metadata injection: This features allows you to use the same transformation logic in multiple scenarios for better efficiency and consistency of data mapping.

Pros of Pentaho Data Integration 

• It offers an interactive user-friendly No-code GUI.
• It provides analytics, and task results to overview good insight of the business.

Cons of Pentaho Data Integration 

• The community edition doesn’t have Scheduler and Job Manager that makes some of the tasks manually.
• Documentation for PDI is not very helpful os implementation becomes hard.

3. CloverETL (CloverDX)

Rating:4.3/5 (G2)

CloverETL is an open-source Data Mapping and Data Integration tool that is built in Java. It can be used to transform, map and manipulate data. It provides flexibility to users to use it as a standalone application, command-line tool, server application or can be embedded in other applications.

CloverETL allows companies to efficiently create, test, deploy and automate the data loading process from source to destination.

Key Features of CloverETL

• It provides visual as well as coding interfaces for developers to map and transform data.
• It provides reusable templates for streamlining complex pipelines
• The repeatable workflows allow you to save time in building multiple pipelines.
• It has the capability to help you conduct reverse ETL jobs.

Pros of CloverETL

• It offers good speed in data transformation.
• Data parallelism data services can be used to create web services.

Cons of CloverETL

• Lack of proper documentation for setup and implementation.
• Less number of files and formats supported.

4. Pimcore

Rating:4.5/5 (G2)

Pimcore is an open-source Data Management software platform that is entirely developed in PHP. It is an enterprise-level Data Mapping tool for content management, customer management, digital commerce, etc. It ensures the availability of up-to-date data to all the team members of a company. 

The Key Features of Pimcore

• It offers an easy data import option from formats such as CSV, XLSX, JSON, XML, and map data without writing any code.
• Users can import data at regular intervals. It also integrates with other product-based websites like E-Commerce platforms, Social Media websites, etc.
• It offers a standard API, a full-featured REST Webservice API, and a Data Hub GraphQL API to connect anything in a two-way and real-time.

Pros of Pimcore

• It can easily integrate with other platforms using web services.
• It offers an enterprise-grade solution for free.

Cons of Pimcore

• Not easy to use for non-technical users.
• The asset portal extension of the DAM module is not compatible with mobile devices.

5. Informatica PowerCenter

Rating:4.5/5 (G2)

Informatica PowerCenter provides a highly scalable Data Integration solution with powerful performance and flexibility. By using its proprietary transformation language users can build custom transformations.
By using its pre-built data connectors for most AWS offerings like  S3/DynamoDB/Redshift etc., users can configure a versatile Data Integration solution for AWS. 

Many compliance and security certifications like SOC/HIPAA/Privacy Shield, are adhered to by Informatica PowerCenter. 

Key Features of Informatica PowerCenter

• Data mappings are regularly updated and managed during data integration. 
• It has a monitoring console that helps to quickly identify and rectify any problems during data mapping and integration.
• It provides further flexibility by allowing you to write custom code in Java or JavaScript.

Pros of Informatica PowerCenter

• Informatica is suited if you have multiple data sources on AWS and have confidential data. It provides a centralized repository where all the data (e.g. databases/flat files/streaming data/network etc. related to sources/targets) is stored.

Cons of Informatica PowerCenter

• Cost of initial licensing and heavy running costs.
•  If you wish to use a Cloud Data Warehouse destination it only supports Amazon Redshift.
• Microsoft Azure SQL Data Lake is the only Data Lake destination it supports.

Simplify your data workflows with Hevo’s reliable and easy-to-use ETL tool. Book a demo today to see how our powerful integrations deliver 100% accurate data with zero latency.

6. IBM InfoSphere

Rating: 4.1/5 (G2)

IBM InfoSphere is and part of the IBM Information Platforms Solutions suite and a Data Integration platform that helps enterprises monitor, cleanse, and transform data. It is highly scalable and flexible when it comes to handling massive volumes of data in real-time.

Key Features of IBM InfoSphere

• It delivers high performance in Data Mapping and loading using its Massively Parallel Processing (MPP) capabilities.
• It provides authoritative views of information with proof of lineage and quality for better visibility and data governance.
• The in-built connectors are capable of managing all phases of an effective data integration project.

Pros of IBM InfoSphere

• It is a versatile and scalable platform to handle massive volumes of data.
• It can easily integrate with other IBM Data Management solutions and adds more flexibility to the features.

Cons of IBM InfoSphere

• IBM InfoSphere is not easy to use and not quickly adaptive.
• It is expensive than many other Data Mapping tools available.

7. Microsoft SQL Integration Services

Rating: 3.8/5 (G2)

Microsoft SQL Server Integration Services is part of Microsoft SQL and a Data Integration and Data Migration tool. It is used for automating the maintenance of SQL server Databasesand updates to multidimensional cube data. The most of workflow of Microsoft SQL Server Integration Services includes coding, and the workspace looks similar to Visual Studio Code.

Key Features of Microsoft SQL

• Microsoft SQL Server Integration Services can perform complex jobs seamlessly and is empowered with a rich set of built-in tasks and transformation tools for constructing packages.
• It provides graphical tools for building packages.
• The Catalog database helps you store, run, and manage packages.

Pros of Microsoft SQL

• It comes with excellent support via Microsoft.
• It offers GUI that helps users easily visualize all the data flow.

Cons of Microsoft SQL

• It requires skilled developers to operate because it supports a coding interface.
• It is not efficient for handling JSON and has fewer Excel connections.

8. WebMethods

Rating: 4.3/5 (G2)

WebMethods Integration Server is a Java-based Integration server for enterprises. It supports many services such as Data Mapping and communication between systems. 

WebMethods Integration Server can serve Data Mapping tasks to On-premise, hybrid, and Cloud. It also supports Java, C, and C++ for more flexibility to users. It is best suited for Data Mapping of B2B solutions.

Key Features of WebMethods

• It uses “Lift & shift” cloud adoption strategy that gets your webMethods integrations to the cloud with no extra hardware costs or installations.
• It provides a library of transformation services to bind your data formats together.

Pros of WebMethods

• It supports Document tracking.
• It is easy to use, scalable and includes most of the enterprise tools (all in one).

Cons of WebMethods

• Expensive for small and mid-sized companies.
• Lack of documentation on legacy systems.

9. Oracle Integration Cloud Service

Rating: 4.1/5 (G2)

ICS is an integration application that can perform Source to Target Mapping between many Cloud-Based applications and data sources. 

It can also go beyond that to include some On-Premise data. It also provides 50+ native app adapters for integrating On-Premise and other application data. 

Key Features of Integration Cloud Service

• It provides run-ready templates to unify workflows and permissions across ERP, HCM, and CX applications.
• It uses Oracle GoldenGate for real-time data integration, replication, and stream analytics.
• It has a well streamlined API full lifecycle management that allows you to design, create, promote, and secure internal or external APIs.

Pros of Oracle Integration Cloud Service

• Both, SaaS Extension and Integration coalesce under one product. 
• Seamlessly integrates with other Oracle offerings like Oracle Sales cloud/API Platform Cloud Service/SPMS etc. 

Cons of Oracle Integration Cloud Service

• It could be overkill for your purpose as it includes the capabilities of Process Automation, Visual Application Building. 
• Its costs could be prohibitive as it’s priced according to the many features it provides. 

10. Dell Boomi AtomSphere

Rating: 4.5/5 (G2)

Dell Boomi AtomSphere is a Cloud-based Data Integration and Data Mapping tool from Dell. With the help of its visual designer, users can easily map data between the 2 platforms and integrate them. Dell Boomi AtomSphere is suitable for companies of all sizes.

Key Features of Dell Boomi AtomSphere

• The tool provides a number of pre-built connectors that help you avoid writing coding.
• It updates and deploys synchronized changes to your integration processes, which takes away the burden of managing the mapping manually.
• It incorporates crowdsourced contributions from the Boomi support team and user community for error handling within the UI.

Pros of Dell Boomi AtomSphere

• It offers drag and drop features which makes the job easier for non-technical users.

Cons of Dell Boomi AtomSphere

• Lack of documentation.
• The point and click feature cannot resolve complex solutions.

11. Talend Cloud Integration

Rating: 4.3/5 (G2)

The open-source version of Talend Open Studio was retired on 31st January 2024. Talend Cloud Integration is an ETL solution that comes with a Data Mapping tool.

Talend Data Mapper allows users to define mapping fields and execute the transformation of data between records of two different platforms. Talend Cloud Integration offers a graphical user interface that makes the tool user-friendly and helps save time.

Key Features of Talend Cloud Integration

• Its data profiling feature helps you identify any issues in data quality and makes sure that the data mapping is consistent and accurate.
• It uses Spark batch processing for data replication which enables efficient and reliable data mapping even for large datasets.

Pros of Talend Cloud Integration

• It offers a drag and drop feature in the tool pallet which makes the job easier.

Cons of Talend Cloud Integration

• It has fewer number integrations with other modules.

12. Jitterbit 

Rating: 4.6/5 (G2)

Jitterbit is a Data Integration and Data Mapping tool that allows enterprises to establish API connections between apps and services. It can automate the Data Mapping process in SaaS applications and on-premise systems. Jitterbit’s Automapper helps you map similar fields and make the transformation lot easier.

Features of Jitterbit

• With the help of its AI features, users can control the interface using speech recognition, real-time language translation, and a recommendation system.
• It has a Management Console that helps for a centralized view for controlling and monitoring workflow integrations and processes.

Pros of Jitterbit

• Most of the configurations are point and click.
• It comes with an easy-to-use interface with great documentation.

Cons of Jitterbit

• Low-quality logging and debugging.

13. MuleSoft Anypoint Platform

Rating: 4.5/5 (G2)

MuleSoft Anypoint Platform is a unified iPaaS Data Mapping tool that helps enterprises to map data between destination and SaaS applications as a source. It uses its own MuleSoft language to create and execute Data Mapping tasks.

Key Features of MuleSoft Anypoint Platform

• It also supports a mobile version that allows users to manage and monitor the Data Mapping and Data Integration tasks remotely.
• The flexibility allows you to deploy this platform in any architecture or environment.
• It helps you manage, secure, and scale all APIs from a single place.

Pros of MuleSoft Anypoint Platform

• It comes with many exciting connectors that save time to write code for new Data Mapping.
• The tool is an IDE that is easy to navigate and makes development and testing easy.

Cons of MuleSoft Anypoint Platform

• It features its own MuleSoft language to create solutions while there are many Data Mapping tools that provide drag and drop features.

14. SnapLogic

Rating: 4.3/5 (G2)

SnapLogic is a Data Migration and Data Mapping tool that can automate most of the Data Mapping fields using its Workflow Builder and Artificial Intelligence. It auto maps data between cloud applications and destinations to keep the streaming data in sync.

Users can track all the Data Migration and Data Mapping activities with the help of visualization and reporting tools..

Key Features of SnapLogic

• It provides Cache Pipeline that help you to set up archetypal references that can be accessed through expression language lookup.
• The tool provides Enhanced Account Encryption that allows you to store and use your own keys for encrypting account information in a Groundplex. 
• By using Email Encryption, the administrators can add keys to encrypt user’s emails.

Pros of SnapLogic

• Data Mapping is easy to implement and provides flexibility to users.
• User-friendly interface and doesn’t require any developer.

Cons of SnapLogic

• It is not suitable for complex pipelines and filed mappings.
• It is expensive for very huge datasets.

Data Mapping Challenges

Despite all the benefits data mapping brings to businesses, it’s not without its own set of challenges.

Mapping data fields

Mapping data fields accurately is critical for achieving the intended results from your data migration process. This can be challenging if the source and destination fields have distinct names or formats (for example, text, integers, and dates).

Furthermore, manually mapping hundreds of different data fields might be time-consuming. Employees may become more prone to mistakes over time, resulting in discrepancies and confusion.

Automated data mapping tools address this issue by incorporating automated workflows into the process.

Technical expertise

Another challenge is that data mapping necessitates knowledge of SQL, Python, R, or another computer language.

Sales and marketing professionals employ a variety of data sources, which must be mapped to uncover meaningful insights. Unfortunately, only a small percentage of these employees can use programming languages.

In most circumstances, they must involve the technical team in the process. However, the IT staff is busy with other duties and may not be able to react to the request right away. Eventually, a simple link between two data sources may take a long time or perhaps become an endless chain of jobs in the developers’ backlog.

Data cleansing and harmonization

Raw data is in no way suitable for data integration. First and foremost, data specialists must clean the basic dataset of duplicates, empty fields, and other useless data.

If done manually, this is a time-consuming and repetitive task. According to the Forbes poll, data scientists devote 80% of their time to data collecting, cleansing, and organization.

This is an unavoidable task. Data integration and migration techniques based on unnormalized data will lead you nowhere.

Handling errors

Errors while data mapping can have serious consequences for data accuracy and integrity in the long run. 

Any data mapping method is subject to error. It could be a typo, a mismatch between data fields, or a change in data format.

To avoid data-related disasters, it is critical to have an error-handling strategy in place.

One solution is to implement an automated quality assurance system. It will monitor data mapping operations and correct problems as they arise. However, in this case, analysts will receive more manual labor because they will still have to manually correct errors.

Additional Resources on Data Mapping Tools

• Big Data Tools
• Data Management Tools
• Data Profiling Tools

Conclusion

This blog gives a fair idea about Data Mapping. It first delved into what Data Mapping actually is, eventually describing the process of Data Mapping, its importance in the ETL process, and the factors a user should consider before picking out a suitable Data Mapping Tool from the market.

It finally talks about the 3 Best Data Mapping Tools spread across Open-Source Tools, Cloud-Based Tools, and On-Premise Tools. Finally, we discuss a tool that provides you with the goodness of most of the above approaches in an economical manner. 

Now you can learn more about Schema Mapping , Source to target Mapping and also know how Hevo automates the tedious task of manually mapping source schema with destination schema.

FAQ on Data Mapping Tools

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