Melissa Data Quality vs Qlik Talend Cloud comparison

Why PeerSpot?

Melissa and Qlik are both solutions in the Data Quality category. Melissa is ranked #11, while Qlik is ranked #2 with an average rating of 7.8. Melissa holds a 3.9% mindshare in DQ, compared to Qlik’s 6.6% 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

3,334 Views
2,300 Comparison Views

95% willing to recommend

Qlik Talend Cloud

Read 56 Qlik Talend Cloud reviews

18,135 Views
3,446 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: September 2026).

Buyer's Guide

Melissa Data Quality vs. Qlik Talend Cloud

September 2026

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Helped 913,806 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

11th

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 (3rd), Cloud Data Integration (6th), Data Governance (9th), Cloud Master Data Management (MDM) (3rd), Streaming Analytics (6th), Integration Platform as a Service (iPaaS) (6th)

Mindshare comparison

As of September 2026, in the Data Quality category, the mindshare of Melissa Data Quality is 3.9%, up from 2.8% compared to the previous year. The mindshare of Qlik Talend Cloud is 6.6%, down from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Mindshare Distribution
Product	Mindshare (%)
Qlik Talend Cloud	6.6%
Melissa Data Quality	3.9%
Other	89.5%

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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Herivao Jus Winny

IT Consultant at a tech services company with 201-500 employees

Has automated recurring data flows and improved accuracy in reporting

The best features of Talend Data Integration are its rich set of components that let you connect to almost any data design intuitive and its strong automation and scheduling capabilities. The TMap component is especially valuable because it allows flexible transformation, joins, and filtering in a single place. I also rely a lot on context variables to manage different environments like Dev, Test, and production, without changing the code. The error handling and logging tools are very helpful for monitoring and troubleshooting, which makes the workflow more reliable. Talend Data Integration has helped our company by automating and standardizing data processes. Before, many of these tasks were done manually, which took more time and often led to errors. With Talend Data Integration, we built automated pipelines that extract, clean, and load data consistently. This not only saves hours of manual effort, but also improves the accuracy and reliability of data. As a result, business teams had faster access to trustworthy information for reporting and decision making, which directly improved efficiency and productivity. Talend Data Integration has had a measurable impact on our organization. By automating daily data loading processes, we reduced manual effort by around three or four hours per day, which saved roughly 60 to 80 hours per month. We also improved data accuracy. Error rates dropped by more than 70% because validation rules were built into the jobs. In addition, reporting teams now receive fresh data at least 50% faster, which means they can make decisions earlier and with more confidence. Overall, Talend Data Integration has increased both efficiency and reliability in our data workflows.

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Quotes from Members

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

Pros

"It has a straightforward, easy setup."

"Allows us to identify cell phones before dialing, and giving us data about callers."

"Since we switched to Melissa Data web services, we do not need to maintain those servers and/or software, and we get the most up-to-date addresses from USPS."

"The pricing is very competitive and the product is robust enough to get the job done."

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

"By validating and parsing the addresses our customers submit to us, we have reduced the number of addressing errors encountered during our processing."

"We like having the ability to write our own utilities/software to process our records and store the final output the way we want."

"The customers' addresses are now complete, correct and follow one consistent format."

More Melissa Data Quality pros

"If your use case depends on heterogeneous sources like Salesforce, SAP, and some other product information systems, I would recommend Talend."

"The solution is customizable."

"It offers advanced features that allow you to create custom patterns and use regular expressions to identify data issues."

"The features that I like the most are the simplicity of the interface, and the ability to quickly develop with a predefined component."

"The solution is connected to various phones. It retrieves the configuration from the system."

"What I like about the Talend MDM Platform is that it's a good vendor diagnostic tool."

"Qlik Talend Cloud positively impacted my organization by enabling businesses to accelerate data decisions very easily and reducing execution time."

"I really like the fact that there are no out-of-the-box solutions regarding the development of jobs. Other vendors may have modules which cleanse your addresses. In Talend, you have the freedom to completely develop the process yourself. This can be tricky, but it also makes it fun."

More Qlik Talend Cloud pros

Cons

"Pricing is based on tiers, with each tier capped at a specified number of records processed. Once you go over the cap at one tier, you are automatically bumped to the next tier. However, they seem to count failed batch processes so it’s good to keep track of the number of records sent. They’ll fix the count when notified, but their system fails to detect actual successful processes versus failed processes."

"We have noticed that some of the emails and addresses return with confusing or incorrect codes, but for the most part, it is accurate."

"The use case I'm familiar with is for a merchant who was contrasting this technology with data from Dun and Bradstreet: so my recommendation is that Melissa Data purchase Dun and Bradstreet to combine their data breadth."

"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."

"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."

"Needs more/better search tools are needed. Also, state and local tax data would be nice."

"Address validation and parsing in a few countries have room for improvement."

"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

"If I am designing something in Talend Data Integration, I cannot backtrack in the flow. For example, if I read data from a source and transform it before applying complex aggregation in the third step, I cannot join this data with the second step."

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

"I would like to sync a project and do an upload from that current version, and then from GitLab, be able to download the latest one."

"If the SQL input controls could dynamically determine the schema-based on the SQL alone, it would simplify the steps of having to use a manually created and saved schema for use in the TMap for the Postgres and Redshift components. This would make things even easier."

"Due to using the open-source version of Talend Data Integration, which lacks a scheduler, our current approach involves developing jobs in Talend, exporting them as Java packages, and utilizing an external scheduler, such as Windows Scheduler, to manage the scheduling process."

"Sometimes when working with larger datasets (possibly due to insufficient memory)."

"The product must enhance the data quality."

"The ability to change the code when debugging the JavaScript could be improved."

More Qlik Talend Cloud cons

Pricing and Cost Advice

"Pricing is very reasonable, no licensing required."

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

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

"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."

"It is affordable."

"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."

"You should have a good idea of the size of your data and the amount of cleansing you will be doing, so you will purchase the appropriate size bundle."

"It's affordable."

More Melissa Data Quality pricing and cost advice

"I would advise to first take a look and at the Open Studio edition. Figure out what you need and purchase the appropriate license."

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

"The tool is cheap."

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

"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."

"I have been using the open-source version."

"License renewal is on a yearly basis."

"The price of the Talend Data Management Platform is reasonable. The other competing solutions are priced high. Gartner Magic Quadrant identified other solutions, such as Informatica, that are far more expensive."

More Qlik Talend Cloud pricing and cost advice

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Top Industries

By visitors reading reviews

Construction Company

18%

Outsourcing Company

15%

Comms Service Provider

10%

Insurance Company

Financial Services Firm

14%

Outsourcing Company

12%

Comms Service Provider

10%

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 5% 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

September 2026

Download Melissa Data Quality product report

Buyer's Guide

Qlik Talend Cloud

September 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

September 2026

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