Melissa Data Quality vs Qlik Talend Cloud comparison

Why PeerSpot?

Melissa and Qlik are both solutions in the Data Quality category. Melissa is ranked #13, while Qlik is ranked #2 with an average rating of 7.9. Melissa holds a 3.9% mindshare in DQ, compared to Qlik’s 6.8% mindshare. Additionally, 95% of Melissa users are willing to recommend the solution, compared to 89% of Qlik users who would recommend it.

Melissa Data Quality

Read 40 Melissa Data Quality reviews

3,805 Views
2,587 Comparison Views

95% willing to recommend

Qlik Talend Cloud

Read 56 Qlik Talend Cloud reviews

21,375 Views
4,061 Comparison Views

89% willing to recommend

Melissa Data Quality

Qlik Talend Cloud

Comparison Buyer's Guide

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Executive SummaryUpdated on Feb 22, 2026

Qlik Talend Cloud and Melissa Data Quality compete in the data integration and data quality sectors. Qlik Talend Cloud has the advantage in pricing and support, while Melissa offers superior features for its price.

Features: Qlik Talend Cloud enhances integration between platforms, streamlines data transformation, and supports scalability and flexibility. Melissa Data Quality excels in data verification and enhancement, specializing in address cleansing, data matching, and deduplication.

Room for Improvement: Qlik Talend Cloud could benefit from enhanced data quality verification tools, additional configuration options for advanced data cleansing, and improved offline functionality during network outages. Melissa Data Quality requires better integration with third-party platforms, more flexible pricing models to accommodate varying budget constraints, and enhancements in user interface for a more intuitive experience.

Ease of Deployment and Customer Service: Qlik Talend Cloud offers a straightforward deployment model with robust customer support and comprehensive documentation. Melissa Data Quality emphasizes dedicated customer service, providing personalized support options and effective deployment solutions.

Pricing and ROI: Qlik Talend Cloud delivers competitive pricing with flexible models, which often result in faster ROI through efficient integration and support. Melissa Data Quality, while possibly more expensive initially, offers long-term cost-effectiveness via precision-focused features that enhance data accuracy and provide significant ROI.

To learn more, read our detailed Melissa Data Quality vs. Qlik Talend Cloud Report (Updated: November 2026).

Buyer's Guide

Melissa Data Quality vs. Qlik Talend Cloud

November 2026

Download the complete report

Helped 920,769 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Melissa Data Quality

Ranking in Data Quality

13th

Ranking in Data Scrubbing Software

4th

Average Rating

8.4

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Qlik Talend Cloud

Ranking in Data Quality

2nd

Ranking in Data Scrubbing Software

1st

Average Rating

8.0

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Data Integration (7th), Master Data Management (MDM) Software (4th), Cloud Data Integration (6th), Data Governance (8th), Cloud Master Data Management (MDM) (3rd), Streaming Analytics (11th), Integration Platform as a Service (iPaaS) (6th)

Mindshare comparison

As of November 2026, in the Data Quality category, the mindshare of Melissa Data Quality is 3.9%, up from 2.7% compared to the previous year. The mindshare of Qlik Talend Cloud is 6.8%, up from 6.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Mindshare Distribution
Product	Mindshare (%)
Qlik Talend Cloud	6.8%
Melissa Data Quality	3.9%
Other	89.3%

Data Quality

Featured Reviews

GaryM

Data Architect at World Vision

SSIS MatchUp Component is Amazing

- Scalability is a limitation as it is single threaded. You can bypass this limitation by partitioning your data (say by alphabetic ranges) into multiple dataflows but even within a single dataflow the tool starts to really bog down if you are doing survivorship on a lot of columns. It's just very old technology written that's starting to show its age since it's been fundamentally the same for many years. To stay relavent they will need to replace it with either ADF or SSIS-IR compliant version. - Licensing could be greatly simplified. As soon as a license expires (which is specific to each server) the product stops functioning without prior notice and requires a new license by contacting the vendor. And updating the license is overly complicated. - The tool needs to provide resizable forms/windows like all other SSIS windows. Vendor claims its an SSIS limitation but that isn't true since pretty much all SSIS components are resizable except theirs! This is just an annoyance but needless impact on productivity when developing new data flows. - The tool needs to provide for incremental matching using the MatchUp for SSIS tool (they provide this for other solutions such as standalone tool and MatchUp web service). We had to code our own incremental logic to work around this. - Tool needs ability to sort mapped columns in the GUI when using advanced survivorship (only allowed when not using column-level survivorship). - It should provide an option for a procedural language (such as C# or VB) for survivor-ship expressions rather than relying on SSIS expression language. - It should provide a more sophisticated ability to concatenate groups of data fields into common blocks of data for advanced survivor-ship prioritization (we do most of this in SQL prior to feeding the data to the tool). - It should provide the ability to only do survivor-ship with no matching (matching is currently required when running data through the tool). - Tool should provide a component similar to BDD to enable the ability to split into multiple thread matches based on data partitions for matching and survivor-ship rather than requiring custom coding a parallel capable solution. We broke down customer data by first letter of last name into ranges of last names so we could run parallel data flows. - Documentation needs to be provided that is specific to MatchUp for SSIS. Most of their wiki pages were written for the web service API MatchUp Object rather than the SSIS component. - They need to update their wiki site documentation as much of it is not kept current. Its also very very basic offering very little in terms of guidelines. For example, the tool is single-threaded so getting great performance requires running multiple parallel data flows or BDD in a data flow which you can figure out on your own but many SSIS practitioners aren't familiar with those techniques. - The tool can hang or crash on rare occasions for unknown reason. Restarting the package resolves the problem. I suspect they have something to do with running on VM (vendor doesn't recommend running on VM) but have no evidence to support it. When it crashes it creates dump file with just vague message saying the executable stopped running.

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Henrique Fsantos

Data & Analytics Engineer at PicPay

Automated data pipelines have improved analytics workflows and provide trusted reporting data

The best features of Qlik Talend Cloud are its connectors to many systems, databases, APIs, and files, including both SQL and NoSQL databases. The scheduling feature allows me to run jobs automatically in the cloud, and the built-in data quality tools help clean and check data. The reusable components enable me to reuse jobs and parts, which saves time, making it easy to build good data pipelines. The feature that has made the biggest difference for me in Qlik Talend Cloud is the scheduling and automation, which helps me run ETL jobs automatically without manual work. This saves time, reduces errors, and makes the pipelines more stable. In my daily work, I can trust that the data is updated on time and ready for reports and analytics. Qlik Talend Cloud has had a positive impact on my organization by automating data integration processes, reducing manual errors, and ensuring reliable and up-to-date data for reports and business decisions. This has improved operational stability, productivity, and trust in the data. Since using Qlik Talend Cloud, we have reduced manual work, resulting in approximately 20% to 30% time savings in data load processes. We have also seen a reduction in operational errors, mainly due to automation and the data quality checks, and a clear improvement in data reliability since the pipelines became more standardized and monitored.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Still, I would advise getting the service without hesitation."

"Be confident that the scalability and load are not going to be an issue with the services. "

"We mainly communicate with our customers via email, so we primarily use it to find a phone number so we can contact them more efficiently. This allows us to talk to them and resolve their issues much more quickly."

"Services for all manner of data-driven organizations, no matter their size or budget."

"Initial setup was fairly straightforward. The documentation was very good in terms of how to integrate and consume the service(s) that we use. It did not take an abundance of time to set up things on our side to use the service."

"It is easy to install and configure, integrates well with Visual Studio Data Tools, generates a unique key for every address it processes, generates correct error codes whenever it corrects an address, and is very reliable."

"We now have less errors on catalog address labels."

"Easy to deploy with the help of my DBAs and Melissa Data tech support."

More Melissa Data Quality pros

"I think Talend is one of the easiest tools for faster implementation compared to other tools."

"The process of upgrading the software is quite easy."

"I like the way that you can use the context variables, and how you can work those context variables to give you values and settings for every development environment, such as PROD, TEST, and DEV."

"The most valuable feature lies in the capability to assign data quality issues to different stakeholders, facilitating the tracking and resolution of defective work."

"We can develop our own code if we do not see the functionality we need."

"It lowers the amount of time in development from weeks to a day."

"The most valuable features of the Talend Data Management Platform are the components."

"Talend's most valuable feature is its ability to generate code and packages efficiently."

More Qlik Talend Cloud pros

Cons

"We are no longer using Melissa Data to clean up our address information as there are free tools that we can use to do the same thing."

"MatchUp seems to be single threaded, and limits the amount of data that can be processed automatically."

"We would appreciate it if there was a larger database so that we could find information more often. For example, we can search for 10 people and only find the information for three of them, if we are lucky."

"It really hasn't given us a phone number for the owner of the property, and that's one thing I'd really like to be getting. Either a phone number or email."

"It could always be cheaper."

"Need to POC point of entry validation."

"It would be helpful if a list of the codes and explanations could be included."

"An area for improvement is where an end customer's address is not found in the Melissa Data database, even though it is a valid address."

More Melissa Data Quality cons

"I encountered scalability issues."

"There are too many functions which could be streamlined."

"The documentation from version to version could be more accurate."

"I think the memory issues we faced when using the 3GB RAM compared to the 4GB RAM computers caused lot of issues."

"The product must enhance the data quality."

"In redundancy analysis, the query is failing to bring non-matched records. This query is an internal script. There is no way (that I know of) to fix this syntax error for future runs."

"Heap space issues plague us consistently. We maxed it out and it runs fine, then it doesn’t, then it does."

"I think they should drive toward AI and machine learning. They could include a machine-learning algorithm for the deduplication."

More Qlik Talend Cloud cons

Pricing and Cost Advice

"Depends on situation. We prefer to have data onsite, but some might prefer web access."

"We are concerned that our own pricing is going up every year for Melissa Data products, but we highly recommend the services for people who are routinely sending out mailings."

"Be sure to determine how the data is priced (record-based versus credit-based or some hybrid of data and services)."

"Generally, the cost is ROI positive, depending on your shipping volume."

"Fully understand your volume, both monthly and annually. Speak with a Melissa account manager, they will put together an effective solution to meet your needs."

"It's affordable."

"I think it's worth the value for me to run it."

"This vendor has no equal in pricing for equivalent functionality."

More Melissa Data Quality pricing and cost advice

"The product pricing is considered very good, especially compared to other data integration tools in the market."

"The licensing cost is about 40,000 Euros a year."

"Moreover, the pricing structure stands out as highly competitive compared to other offerings in the market, making it a cost-effective choice for users."

"The pricing is a little higher than what I had expected, but it's comparable with I-PASS competitors."

"The tool is cheap."

"We did not purchase a separate license for DQ. It is part of our data platform suite, and I believe it is well-priced."

"It is cheaper than Informatica. Talend Data Quality costs somewhere between $10,000 to $12,000 per year for a seat license. It would cost around $20,000 per year for a concurrent license. It is the same for the whole big data solution, which comes with Talend DI, Talend DQ, and TDM."

"License renewal is on a yearly basis."

More Qlik Talend Cloud pricing and cost advice

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Top Industries

By visitors reading reviews

Outsourcing Company

16%

Construction Company

15%

Comms Service Provider

Healthcare Company

Financial Services Firm

14%

Outsourcing Company

12%

Comms Service Provider

11%

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	3
Large Enterprise	14

By reviewers
Company Size	Count
Small Business	21
Midsize Enterprise	12
Large Enterprise	20

Questions from the Community

Ask a question

Earn 20 points

What needs improvement with Talend Data Quality?

I don't use the automated rule management feature in Talend Data Quality that much, so I cannot provide much feedback. I may not know what Talend Data Quality can improve for data quality. I'm not ...

See all answers

What is your primary use case for Talend Data Quality?

It is for consistency, mainly; data consistency and data quality are our main use cases for the product. Data consistency is the primary purpose we use it for, as we have written rules in Talend Da...

See all answers

What advice do you have for others considering Talend Data Quality?

Currently, I'm working with batch jobs and don't perform real-time data quality monitoring because of the large data volume. For real-time, we use a different product. I cannot provide details abou...

See all answers

Comparisons

Experian Data Quality vs Melissa Data Quality

Compared 12% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Melissa Data Quality

Compared 6% of the time

BCC Data Quality vs Melissa Data Quality

Compared 6% of the time

Ataccama ONE Platform vs Melissa Data Quality

Compared 4% of the time

Precisely Trillium vs Melissa Data Quality

Compared 4% of the time

More Melissa Data Quality Competitors

Informatica Intelligent Data Management Cloud (IDMC) vs Qlik Talend Cloud

Compared 3% of the time

Databricks vs Qlik Talend Cloud

Compared 2% of the time

Ataccama ONE Platform vs Qlik Talend Cloud

Compared 2% of the time

SSIS vs Qlik Talend Cloud

Compared 2% of the time

IBM webMethods Hybrid Integration vs Qlik Talend Cloud

Compared 2% of the time

More Qlik Talend Cloud Competitors

Product Reports

Buyer's Guide

Melissa Data Quality

October 2026

Download Melissa Data Quality product report

Buyer's Guide

Qlik Talend Cloud

October 2026

Download Qlik Talend Cloud product report

Also Known As

No data available

Talend Data Quality, Talend Data Management Platform, Talend MDM Platform, Talend Data Streams, Talend Data Integration, Talend Data Integrity and Data Governance

Overview

Melissa Data Quality delivers robust features for address validation and data standardization with seamless SSIS integration, making it a cost-effective choice for managing large datasets on-premises or in the cloud.

Emphasizing efficiency, Melissa Data Quality supports organizations in refining data accuracy through features like address validation, parsing, and cleansing. Its integration with SSIS simplifies setup and enhances operational ease, while solutions like Personator provide comprehensive contact detail acquisition. The system's match process ensures accurate deduplication, catering to extensive datasets with flexibility from on-premises to cloud deployments. Despite its strengths, there could be improvements in handling unknown addresses, phone number standardization, and international support, alongside refining processing speed and streamlining license management.

What features does Melissa Data Quality offer?

Address Validation: Ensures accurate address data
Data Parsing: Breaks data into structured components
Data Cleansing: Removes inaccuracies and standardizes
SSIS Integration: Simplifies installation and operation
Personator: Extracts names, phone numbers, and emails

What benefits can be expected from Melissa Data Quality?

Cost Efficiency: Provides value for business data needs
Accuracy: Enhances precision through deduplication
Flexibility: Adapts from on-premises to cloud solutions
Contact Verification: Streamlines onboarding processes

Organizations employ Melissa Data Quality for accurate address validation, customer data accuracy, and geocoding. It's instrumental in duplicate identification, data cleansing, and maintaining address accuracy via USPS NCOA. During customer onboarding, it verifies details while integrating seamlessly with existing data systems, using Listware and Personator for precise address entry, geocoding, and status updates, helping classify businesses by industry.

Melissa

Qlik Talend Cloud provides robust data integration tools tailored for efficient management of large volumes, offering real-time data access, Java integration, and custom code capabilities for developers.

Qlik Talend Cloud is known for its extensive connectivity options, enabling seamless integration across different platforms, such as S3, Redshift, Oracle, and SQL Server. The central repository facilitates consistent metadata access throughout organizations, enhancing collaboration. Despite its strengths in advanced monitoring, automation, and user-friendly drag-and-drop interfaces, users face challenges with installation stability, technical support, documentation inconsistencies, and complexities in learning. Performance concerns also include multitasking limitations and excessive memory usage. The platform's licensing costs can be prohibitive for smaller companies, while demands for improved data governance and intuitive code management continue. Its applications in healthcare data parsing, ETL task automation, and diverse data platform integration demonstrate its utility, although there's a constant demand for better scalability and efficient transformations.

What are the key features?

Seamless Real-Time Data Access: Ensures that users can access the latest information across systems without delays for better decision-making.
Java Integration: Facilitates smooth incorporation of Java components into workflows, broadening development options.
Custom Code Development: Offers flexibility to develop tailored solutions to meet specific integration scenarios.
Vast Connectors: Enhances connectivity between multiple platforms, supporting comprehensive data integration strategies.
Central Repository: Allows for uniform metadata access across departments, promoting cohesive data management practices.
Automation and Monitoring: Streamlines workflows with advanced automation and monitoring, reducing manual intervention and errors.

What benefits and ROI should users evaluate?

Improved Data Quality: Tools for data matching and error handling ensure high-quality, reliable data for analytics and reporting.
Efficient Workflow Streamlining: Drag-and-drop interfaces and automation features simplify complex workflows, enhancing operational efficiency.
Enhanced Business Insights: By integrating diverse data sources, businesses can derive deeper insights and ensure compliance through better data management.
Cost Management: Although some users find licensing expensive, comprehensive feature sets aimed at improving data processes can justify costs.
Community Support: Users benefit from active community forums that provide assistance, although technical support also necessitates enhancements.

In specialized industries like healthcare, users leverage Qlik Talend Cloud for data integration and transformation, aiding in compliance and analytics. Compatibility with cloud and on-premises systems ensures adaptability to complex data tasks, facilitating business application development. Organizations focus on enhanced data ingestion and quality checks for comprehensive solutions.

Qlik

Sample Customers

Boeing Co., FedEx, Ford Motor Co, Hewlett Packard, Meade-Johnson, Microsoft, Panasonic, Proctor & Gamble, SAAB Cars USA, Sony, Walt Disney, Weight Watchers, and Intel.

Aliaxis, Electrocomponents, M¾NCHENER VEREIN, The Sunset Group

Buyer's Guide

Melissa Data Quality vs. Qlik Talend Cloud

November 2026

Free Report: Melissa Data Quality vs. Qlik Talend Cloud

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