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

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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

"The high value in this tool is its relatively low cost, ease of use, tight integration with SSIS, superior performance (compared to competitors), and attribute-level advanced survivor-ship logic."

"It saves a huge amount of time. Before using this service, we used a vendor that manually ran our lists through this NCOA list, which might have taken one to three business days to return the file. This was a huge bottleneck in our process, and the data returned was not always accurate. After switching to Melissa Data’s SmartMover, the process has been reduced to between ten minutes and three hours, depending on the amount of records sent."

"Helps our organization provide accurate address information to our customers for direct mailing (household) and other campaigns they want to do."

"Personator application was able to append emails, new address if moved, phone number, geocode, and also standardizes existing customer information."

"This tool works better for us than using a batch processing system that we do not have enough control over as each record is being processed."

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

"I was able to dedupe millions of records in the past, and append the most recent email."

"Customer service was excellent, and their technical team provides top support for people wanting to use the technology."

More Melissa Data Quality pros

"I like idea of storing the results of Data Quality jobs in a DB and having the ability to run reports in the DB to show a dashboard of quality metrics."

"The file fetch process is impeccable."

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

"It is saving a lot of time. Today, we can mask around a hundred million records in 10 minutes. Masking is one of the key pieces that is used heavily by the business and IT folks. Normally in the software development life cycle, before you project anything into the production environment, you have to test it in the test environment to make sure that when the data goes into production, it works, but these are all production files. For example, we acquired a new company or a new state for which we're going to do the entire back office, which is related to claims processing, payments, and member enrollment every year. If you get the production data and process it again, it becomes a compliance issue. Therefore, for any migrations that are happening, we have developed a new capability called pattern masking. This feature looks at those files, masks that information, and processes it through the system. With this, there is no PHI and PII element, and there is data integrity across different systems. It has seamless integration with different databases. It has components using which you can easily integrate with different databases on the cloud or on-premise. It is a drag and drop kind of tool. Instead of writing a lot of Java code or SQL queries, you can just drag and drop things. It is all very pictorial. It easily tells you where the job is failing. So, you can just go quickly and figure out why it is happening and then fix it."

"Extract, Transform, and Load (ETL) transformations are open source and is a valuable feature."

"The wide range of components which suits a variety of purposes and provides a flexible development environment to the coder."

"The availability of connectors is great."

"The primary use case is for data ingestion."

More Qlik Talend Cloud pros

Cons

"Speed of delivery/ease of use. They advertise a 24-hour, next business day turn time on data annotation, but I’ve found it is usually closer to 72 hours. This is still excellent, just make sure you add in the appropriate fluff to your delivery timelines."

"If I had multiple Excel files open and ran Listware it would crash Excel, charge the credits, and not save the results."

"More countries should be supported by Melissa."

"It would be great if the product can be expanded to standardize and clean Telephone Numbers and TaxID’s/SSN’s."

"Many issues, sometimes I have to completely log out and start over."

"Overall there is a room for improvement in Customer Address Appends and Email Appends."

"Needs better email append coverage (but every vendor struggles with this)."

"MatchUp is a more complex product and I recommend a test area before upgrading to production. Performance can change from version to version."

More Melissa Data Quality cons

"Data management could also encompass APIs and real-time streaming data integration."

"SQL for displaying underlying data in non-match results does not work."

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

"The stability is good, but the performance is slower when I work on a huge amount of data."

"Lots of errors and system crashes during installation and IMHO we didn’t receive good enough technical support and guidance from Talend to solve the issues."

"They don't have any AI capabilities. Talend DQ is specifically for data quality, which only has data profiling. With Talend DQ, I cannot generate any reports today, so I need an ETL tool. It provides general Excel files, or I have to create some views. If instead of buying a new tool, Talend provides a reporting capability or solution, it would be great. It will reduce the development effort for creating these kinds of reports. We also manage the infrastructure for Talend. From the licensing perspective, for cloud, they only have seat licenses where one person is tied to one license, but for on-premise, they have concurrent licenses. It would be really awesome if they can provide concurrent licenses for the cloud so that if one person is not there, somebody else can use that license. Currently, it is not possible unless a person deactivates his or her license and moves the same seat license to someone else. We are one of the biggest customers in the central zone of the US for Talend, and this is the feedback that we have provided them again and again, but they come back and say that they aren't able to provide concurrent licenses on the cloud. In version 7.3, there is a feature for tokenization and de-tokenization of data. This is the feature that we are looking for. It is useful if somebody wants to see what we have masked and how do we demask it. This feature is not there in version 7.1. There are also a few other capabilities on the cloud, but we don't yet have a big footprint in the cloud."

"There are no natural connections for some of the applications that I use more regularly."

"I think the subscription-based model is concerning because as I mentioned, some of our other projects are migrating to different tools."

More Qlik Talend Cloud cons

Pricing and Cost Advice

"Pricing is very reasonable."

"It is affordable."

"They were willing to work with our preferred vendors, though it involved extra steps to get the license."

"The only complaint that I have towards it is they sell licenses based on a range of usage, and I feel those ranges are too large."

"Trial subscriptions (via cloud) are very cheap and easy to use. It’s a great way to test Listware to see if you want to go deeper with integration."

"Melissa pricing is competitive."

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

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

More Melissa Data Quality pricing and cost advice

"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's a subscription-based platform, we renew it every year."

"The price is on a per-user basis. It's a little more expensive than other tools. There aren't any additional costs beyond the standard licensing fee."

"The licensing cost for the Talend MDM Platform is paid yearly, but I'm unable to give you the figure. I would rate its price as four out of five because it's on the cheaper side. I'm not aware of any extra costs in addition to the standard licensing fees for the Talend MDM Platform."

"The tool is cheap."

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

"License renewal is on a yearly basis."

"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."

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

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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

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