Melissa Data Quality vs Qlik Talend Cloud comparison

Melissa and Qlik are both solutions in the Data Quality category. Melissa is ranked #7, while Qlik is ranked #2 with an average rating of 8.0. Melissa holds a 4.4% mindshare in DQ, compared to Qlik’s 6.8% mindshare. Additionally, 95% of Melissa users are willing to recommend the solution, compared to 88% of Qlik users who would recommend it.

Melissa Data Quality

Read 40 Melissa Data Quality reviews

1,943 Views
1,360 Comparison Views

95% willing to recommend

Qlik Talend Cloud

Read 55 Qlik Talend Cloud reviews

10,346 Views
1,966 Comparison Views

88% 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: April 2026).

Buyer's Guide

Melissa Data Quality vs. Qlik Talend Cloud

April 2026

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Helped 886,932 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

7th

Ranking in Data Scrubbing Software

5th

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 (5th), Master Data Management (MDM) Software (3rd), Cloud Data Integration (6th), Data Governance (8th), Cloud Master Data Management (MDM) (4th), Streaming Analytics (8th), Integration Platform as a Service (iPaaS) (6th)

Mindshare comparison

As of April 2026, in the Data Quality category, the mindshare of Melissa Data Quality is 4.4%, up from 2.6% compared to the previous year. The mindshare of Qlik Talend Cloud is 6.8%, down from 10.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Mindshare Distribution
Product	Mindshare (%)
Qlik Talend Cloud	6.8%
Melissa Data Quality	4.4%
Other	88.8%

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

"Standardizing allows me to more effectively check for duplicate/existing records. Verifying increases the value of the data."

"Decreased chance of incorrect shipping addresses and, thus, returned packages."

"Melissa offer a high quality product with great service."

"Extremely easy to install and setup."

"This serves our single need and we may utilize Melissa Data for other lookups, such as validate address lookup, in the future."

"It gives me an assessed value of the property in question. My partner and I are property investors, and it's good to get an assessed value to cull out properties that we're not interested in."

"Address verification ensures our customers get their packages, and we aren’t charged for incomplete address information."

"We use a Melissa API to access the data, so it easy to use, accurate, and fast."

More Melissa Data Quality pros

"We have used value frequency and patterns. We have been it impressed with these functions as they have helped us in making decisions in transformation work."

"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 best features Qlik Talend Cloud offers include the fact that it is built on Java, which gives me the chance to customize my requirements and write my own Java code to achieve my logic."

"Talend Data integration has a wide library of connectors."

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

"Provides a flexible development environment to the coder."

"Flexibility is a key feature I appreciate about Talend Data Integration, especially the integration of Java within it and the ease of integrating with multiple source repositories such as GitHub and Bitbucket."

"Some of the algorithms that are inbuilt in Talend Data Quality, such as Levenshtein, are the most valuable functions for us."

More Qlik Talend Cloud pros

Cons

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

"It could always be cheaper."

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

"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."

"It would be nice if it also had a user interface, as it did in years past."

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

"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."

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

More Melissa Data Quality cons

"I'd be interested in seeing the running of Python programs and transformations from within the studio itself."

"Account for Java developers/custom development efforts apart from DQ functional/technical expertise, to use Talend DQ product to the fullest."

"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."

"I would say that some of the support elements need improvement."

"There are more functions in a non-streamlined manner, which could be refined to arrive at a better off-the-shelf functions."

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

"I wonder if, at the same price, the API component could be added, which would be beneficial."

"In terms of the solution's technical support, the interactions were satisfactory, but there is room for improvement, especially in managing expectations."

More Qlik Talend Cloud cons

Pricing and Cost Advice

"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."

"It is affordable."

"Pricing is very reasonable, no licensing required."

"Pricing is very reasonable."

"Understand how may transactions you will be processing so that you can get the right tier pricing."

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

"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."

More Melissa Data Quality pricing and cost advice

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

"The tool is cheap."

"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 solution's pricing is very reasonable and half the cost of Informatica."

"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."

"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."

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

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

More Qlik Talend Cloud pricing and cost advice

See which vendors are best for you

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Top Industries

By visitors reading reviews

Construction Company

11%

Insurance Company

11%

Computer Software Company

Manufacturing Company

Financial Services Firm

16%

Computer Software Company

Comms Service Provider

Government

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	20
Midsize Enterprise	11
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 ...

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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...

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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...

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Comparisons

Experian Data Quality vs Melissa Data Quality

Compared 14% 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

Anchor Software Data Quality vs Melissa Data Quality

Compared 4% of the time

Coheris Spad Data Quality vs Melissa Data Quality

Compared 4% of the time

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

Compared 4% of the time

SSIS vs Qlik Talend Cloud

Compared 3% of the time

n8n vs Qlik Talend Cloud

Compared 2% 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

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Product Reports

Buyer's Guide

Melissa Data Quality

April 2026

Download Melissa Data Quality product report

Buyer's Guide

Qlik Talend Cloud

April 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

Data Quality Components for SSIS

This suite of data transformations for Microsoft SQL Server Integration Services (SSIS) delivers the full spectrum of data quality including data profiling, data verification, data enrichment and data matching. With an intuitive interface and drag/drop capabilities, this powerful toolkit makes it easy to unify data into a single version of the truth for Master Data Management (MDM) success.

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

April 2026

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