StreamSets vs dbt comparison

dbt Labs and IBM are both solutions in the Data Integration category. dbt Labs is ranked #27 with an average rating of 7.5, while IBM is ranked #22 with an average rating of 9.0. dbt Labs holds a 1.7% mindshare in DI, compared to IBM’s 1.2% mindshare. Additionally, 100% of dbt Labs users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

dbt

Read 5 dbt reviews

1,642 Views
1,560 Comparison Views

100% willing to recommend

StreamSets

Read 21 StreamSets reviews

2,665 Views
1,919 Comparison Views

100% willing to recommend

dbt

StreamSets

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Executive SummaryUpdated on Dec 19, 2024

StreamSets and dbt are competing products in the data integration and transformation space. StreamSets has an advantage in customer service and pricing, while dbt holds the upper hand in its feature set, promising better long-term value.

Features: StreamSets offers robust data integration with real-time data processing, supports on-premise and cloud environments, and facilitates broad data handling. Dbt focuses on transformation within the warehouse, provides modular SQL development, version control, and specializes in SQL-based transformations.

Ease of Deployment and Customer Service: StreamSets allows for seamless integration with existing systems, with flexible deployment options across on-premise, cloud, and hybrid models. Their customer service is responsive and helpful. Dbt primarily focuses on cloud-based deployment, boasts strong documentation for ease of use, but may require more initial setup. Their SQL-only operations simplify deployment for some, though StreamSets offers greater deployment flexibility and support.

Pricing and ROI: StreamSets provides competitive pricing with various subscription models, offering lower initial costs and quicker ROI. Dbt may be more expensive at the outset; however, its advanced transformation capabilities are seen as a worthwhile investment for optimizing warehouse data, with a promise of long-term value. StreamSets has an advantage in initial cost-effectiveness, while dbt justifies higher costs with its comprehensive features.

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

Buyer's Guide

StreamSets vs. dbt

December 2025

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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

dbt

Ranking in Data Integration

27th

Average Rating

7.8

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Data Quality (17th)

StreamSets

Ranking in Data Integration

22nd

Average Rating

8.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Data Integration category, the mindshare of dbt is 1.7%, up from 0.7% compared to the previous year. The mindshare of StreamSets is 1.2%, down from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Market Share Distribution
Product	Market Share (%)
StreamSets	1.2%
dbt	1.7%
Other	97.1%

Data Integration

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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SrinivasanSankar

Enterprise Solutions Architect at a energy/utilities company with 1,001-5,000 employees

Enables effective batch loading with visual interface and enterprise support

One issue I observed with StreamSets is that the memory runs out quickly when processing large volumes of data. Because of this memory issue, we have to upgrade our EC2 boxes in the Amazon AWS infrastructure. I had to switch to a new EC2 box, even though the processor was not fully utilized. It would be beneficial if StreamSets addressed any potential memory leak issues to prevent unnecessary upgrades. Additionally, it would be a great enhancement if StreamSets could produce a lineage graph to visualize how the data has passed through the system.

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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

"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."

"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."

"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."

"It is really easy to set up and the interface is easy to use."

"I have used Data Collector, Transformer, and Control Hub products from StreamSets. What I really like about these products is that they're very user-friendly. People who are not from a technological or core development background find it easy to get started and build data pipelines and connect to the databases. They would be comfortable like any technical person within a couple of weeks."

"The ETL capabilities are very useful for us. We extract and transform data from multiple data sources, into a single, consistent data store, and then we put it in our systems. We typically use it to connect our Apache Kafka with data lakes. That process is smooth and saves us a lot of time in our production systems."

"The most valuable features are the option of integration with a variety of protocols, languages, and origins."

"StreamSets’ data drift resilience has reduced the time it takes us to fix data drift breakages. For example, in our previous Hadoop scenario, when we were creating the Sqoop-based processes to move data from source to destinations, we were getting the job done. That took approximately an hour to an hour and a half when we did it with Hadoop. However, with the StreamSets, since it works on a data collector-based mechanism, it completes the same process in 15 minutes of time. Therefore, it has saved us around 45 minutes per data pipeline or table that we migrate. Thus, it reduced the data transfer, including the drift part, by 45 minutes."

"One of the things I like is the data pipelines. They have a very good design. Implementing pipelines is very straightforward. It doesn't require any technical skill."

"The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customize it to do what you need. Many other tools have started to use features similar to those introduced by StreamSets, like automated workflows that are easy to set up."

"The scheduling within the data engineering pipeline is very much appreciated, and it has a wide range of connectors for connecting to any data sources like SQL Server, AWS, Azure, etc. We have used it with Kafka, Hadoop, and Azure Data Factory Datasets. Connecting to these systems with StreamSets is very easy."

More StreamSets pros

Cons

"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."

"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."

"The execution engine could be improved. When I was at their session, they were using some obscure platform to run. There is a controller, which controls what happens on that, but you should be able to easily do this at any of the cloud services, such as Google Cloud. You shouldn't have any issues in terms of how to run it with their online development platform or design platform, basically their execution engine. There are issues with that."

"One thing that I would like to add is the ability to manually enter data. The way the solution currently works is we don't have the option to manually change the data at any point in time. Being able to do that will allow us to do everything that we want to do with our data. Sometimes, we need to manually manipulate the data to make it more accurate in case our prior bifurcation filters are not good. If we have the option to manually enter the data or make the exact iterations on the data set, that would be a good thing."

"One issue I observed with StreamSets is that the memory runs out quickly when processing large volumes of data. Because of this memory issue, we have to upgrade our EC2 boxes in the Amazon AWS infrastructure."

"We often faced problems, especially with SAP ERP. We struggled because many columns weren't integers or primary keys, which StreamSets couldn't handle. We had to restructure our data tables, which was painful. Also, pipeline failures were common, and data drifting wasn't addressed, which made things worse. Licensing was another issue we encountered."

"Currently, we can only use the query to read data from SAP HANA. What we would like to see, as soon as possible, is the ability to read from multiple tables from SAP HANA. That would be a really good thing that we could use immediately. For example, if you have 100 tables in SQL Server or Oracle, then you could just point it to the schema or the 100 tables and ingestion information. However, you can't do that in SAP HANA since StreamSets currently is lacking in this. They do not have a multi-table feature for SAP HANA. Therefore, a multi-table origin for SAP HANA would be helpful."

"Sometimes, it is not clear at first how to set up nodes. A site with an explanation of how each node works would be very helpful."

"If you use JDBC Lookup, for example, it generally takes a long time to process data."

"In terms of the product, I don't think there is any room for improvement because it is very good. One small area of improvement that is very much needed is on the knowledge base side. Sometimes, it is not very clear how to set up a certain process or a certain node for a person who's using the platform for the first time."

More StreamSets cons

Pricing and Cost Advice

"The solution’s pricing is affordable."

"It has a CPU core-based licensing, which works for us and is quite good."

"StreamSets is an expensive solution."

"We are running the community version right now, which can be used free of charge."

"StreamSets Data Collector is open source. One can utilize the StreamSets Data Collector, but the Control Hub is the main repository where all the jobs are present. Everything happens in Control Hub."

"The overall cost is very flexible so it is not a burden for our organization... However, the cost should be improved. For small and mid-size organizations it might be a challenge."

"There are different versions of the product. One is the corporate license version, and the other one is the open-source or free version. I have been using the corporate license version, but they have recently launched a new open-source version so that anybody can create an account and use it. The licensing cost varies from customer to customer. I don't have a lot of input on that. It is taken care of by PMO, and they seem fine with its pricing model. It is being used enterprise-wide. They seem to have got a good deal for StreamSets."

"The licensing is expensive, and there are other costs involved too. I know from using the software that you have to buy new features whenever there are new updates, which I don't really like. But initially, it was very good."

"There are two editions, Professional and Enterprise, and there is a free trial. We're using the Professional edition and it is competitively priced."

More StreamSets 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

Financial Services Firm

Manufacturing Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	11

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...

See all answers

What do you like most about StreamSets?

The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customiz...

See all answers

What needs improvement with StreamSets?

See all answers

What is your primary use case for StreamSets?

We are using StreamSets for batch loading.

See all answers

Comparisons

SSIS vs dbt

Compared 20% of the time

Oracle Data Integrator (ODI) vs dbt

Compared 9% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs dbt

Compared 7% of the time

Informatica PowerCenter vs dbt

Compared 7% of the time

More dbt Competitors

Confluent vs StreamSets

Compared 8% of the time

Informatica PowerCenter vs StreamSets

Compared 7% of the time

webMethods.io vs StreamSets

Compared 6% of the time

Oracle GoldenGate vs StreamSets

Compared 6% of the time

More StreamSets Competitors

Product Reports

Buyer's Guide

Data Integration

January 2026

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Buyer's Guide

StreamSets

January 2026

Download StreamSets 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

StreamSets is a data integration platform that enables organizations to efficiently move and process data across various systems. It offers a user-friendly interface for designing, deploying, and managing data pipelines, allowing users to easily connect to various data sources and destinations. StreamSets also provides real-time monitoring and alerting capabilities, ensuring that data is flowing smoothly and any issues are quickly addressed.

IBM

Sample Customers

Information Not Available

Availity, BT Group, Humana, Deluxe, GSK, RingCentral, IBM, Shell, SamTrans, State of Ohio, TalentFulfilled, TechBridge

Buyer's Guide

StreamSets vs. dbt

December 2025

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