Melissa Data Quality vs dbt comparison

dbt Labs and Melissa are both solutions in the Data Quality category. dbt Labs is ranked #17 with an average rating of 7.5, while Melissa is ranked #5 with an average rating of 9.0. dbt Labs holds a 1.5% mindshare in DQ, compared to Melissa’s 3.4% mindshare. Additionally, 100% of dbt Labs users are willing to recommend the solution, compared to 95% of Melissa users who would recommend it.

dbt

Read 5 dbt reviews

1,642 Views
197 Comparison Views

100% willing to recommend

Melissa Data Quality

Read 40 Melissa Data Quality reviews

1,022 Views
623 Comparison Views

95% willing to recommend

dbt

Melissa Data Quality

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Executive Summary

We performed a comparison between Melissa Data Quality and dbt based on real PeerSpot user reviews.

Find out in this report how the two Data Quality solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Melissa Data Quality vs. dbt Report (Updated: December 2025).

Buyer's Guide

Melissa Data Quality vs. dbt

December 2025

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Helped 881,114 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

dbt

Ranking in Data Quality

17th

Average Rating

7.8

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Data Integration (27th)

Melissa Data Quality

Ranking in Data Quality

5th

Average Rating

8.4

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Data Scrubbing Software (5th)

Mindshare comparison

As of January 2026, in the Data Quality category, the mindshare of dbt is 1.5%, up from 0.8% compared to the previous year. The mindshare of Melissa Data Quality is 3.4%, up from 2.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Quality Market Share Distribution
Product	Market Share (%)
Melissa Data Quality	3.4%
dbt	1.5%
Other	95.1%

Data Quality

Featured Reviews

Shubham-Agarwal

Manager Projects at Cognizant

Incremental data models have cut full refresh time and support trusted executive reporting

I am not very familiar with dbt's version control system. I cannot identify any improvements in dbt because I am still exploring more functionality. I have been working with dbt for only three years, so I am exploring more functionalities and cannot see any limitations or improvement areas at this time. In the past, I used the seed functionality, which is used to load raw files, individual files, or static files into the database. Going forward, if dbt can improve or implement more features on the seed side, that would be beneficial, especially when we have large files available that take time to load the data into Snowflake database.

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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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Quotes from Members

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

Pros

"Since we migrated from SSIS to dbt model architecture, it takes around four hours only to complete a full refresh, and the client is now happy because our downtime was drastically reduced when we perform a complete refresh of the data."

"The product is developer-friendly."

"dbt has positively impacted my organization by allowing us to create our data pipelines much faster, going from ingestion of data to creating a data product in weeks instead of months, and we can do it in-house with the skillset we already have."

"There is operational efficiency achieved, and data quality and governance have also been achieved with modular SQL and version controlling, which reduced duplication of data and data errors."

"Decreases chances of incorrect shipping addresses and, thus, returned packages."

"Our customer database is now significantly more accurate and reliable."

"Provides simplicity, ease of use, combined with overall accuracy of data."

"Extremely easy to install and setup."

"We are able to more accurately identify valid, and better formatted, data which improves the data we store in our database."

"Through more accurate data, our marketing department has been able to increase delivery and conversion rates through email direct marketing initiatives."

"Allows us to delete and correct incorrect data to make the searching of our applicant tracking system more consistent and relevant."

"Provides quality accurate data that our downstream solutions depend on."

More Melissa Data Quality pros

Cons

"Since dbt has a license cost, if a company is small and does not have much budget, they can explore other tools because there are other tools that provide the same functionality at a lower cost."

"dbt can be improved as I find the co-pilot in dbt is not very good, and my team has tried using it but opted to move off it and use other co-pilots such as GitHub."

"The solution must add more Python-based implementations."

"Dbt is not as stable as preferred, as it has had a few outages in the current year itself, so improvement should be made in the outages section as it is not stable."

"The billing structure does not seem very accurate. We’ve had issues with miscounted batch records processed"

"The SSIS component setup seems a little klunky."

"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."

"Needs to validate more addresses accurately."

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

"Did not work as advertized. Needs better results in address parsing, as described on the website."

"There are some companies out there using Google or other sources to check / confirm if addresses are residential. If Melissa is not doing this, that could be an improvement."

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

More Melissa Data Quality cons

Pricing and Cost Advice

"The solution’s pricing is affordable."

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

"Cloud version is very cheap. On-premise version is expensive."

"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."

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

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

"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."

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

More Melissa Data Quality pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

14%

Insurance Company

Manufacturing Company

Computer Software Company

Insurance Company

15%

Manufacturing Company

Educational Organization

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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

Questions from the Community

What is your experience regarding pricing and costs for dbt?

My experience with pricing, setup cost, and licensing was simple enough.

See all answers

What needs improvement with dbt?

dbt can be improved as I find the co-pilot in dbt is not very good, and my team has tried using it but opted to move off it and use other co-pilots such as GitHub. Additionally, the debugging capab...

See all answers

What is your primary use case for dbt?

My main use case for dbt is for data transformation and data engineering.A specific example of how I use dbt for data transformation and engineering is that we use it to connect and ingest data fro...

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Comparisons

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Compared 20% of the time

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Compared 9% of the time

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Compared 7% of the time

Informatica PowerCenter vs dbt

Compared 7% of the time

Tobiko vs dbt

Compared 7% of the time

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Compared 24% of the time

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Compared 14% of the time

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

Compared 10% of the time

Coheris Spad Data Quality vs Melissa Data Quality

Compared 7% of the time

Anchor Software Data Quality vs Melissa Data Quality

Compared 7% of the time

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Product Reports

Buyer's Guide

Data Integration

January 2026

Download dbt product report

Buyer's Guide

Melissa Data Quality

January 2026

Download Melissa Data Quality product report

Overview

dbt is a transformational tool that empowers data teams to quickly build trusted data models, providing a shared language for analysts and engineering teams. Its flexibility and robust feature set make it a popular choice for modern data teams seeking efficiency.

Designed to integrate seamlessly with the data warehouse, dbt enables analytics engineers to transform raw data into reliable datasets for analysis. Its SQL-centric approach reduces the learning curve for users familiar with it, allowing powerful transformations and data modeling without needing a custom backend. While widely beneficial, dbt could improve in areas like version management and support for complex transformations out of the box.

What are the most valuable features of dbt?

SQL-Based Development: Facilitates transformations using SQL, streamlining the data modeling process.
Modularity: Encourages reusable components, making projects easier to manage and scale.
Version Control: Integrates with version control systems to track changes and collaborate effectively.
Testing Framework: Ensures data quality through automated tests, increasing trust in data outputs.

What benefits should you expect from using dbt?

Efficiency: Cuts down data processing times, allowing faster access to insights.
Collaboration: Promotes teamwork through shared data models and a common working language.
Scalability: Supports growing data demands by transforming complex data more flexibly and modularly.
Transparency: Provides clear documentation and lineage tracking, aiding in troubleshooting and understanding.

In the finance industry, dbt helps in cleansing and preparing transactional data for analysis, leading to more accurate financial reporting. In e-commerce, it empowers teams to rapidly integrate and analyze customer behavior data, optimizing marketing strategies and improving user experience.

dbt Labs

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

Sample Customers

Information Not Available

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

Buyer's Guide

Melissa Data Quality vs. dbt

December 2025

Free Report: Melissa Data Quality vs. dbt

Find out what your peers are saying about Melissa Data Quality vs. dbt and other solutions. Updated: December 2025.

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