Teradata vs dbt comparison

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Executive SummaryUpdated on Jan 4, 2026

Teradata and dbt compete in the data management and transformation category. dbt appears to have the upper hand for teams seeking a flexible and community-driven approach with easier deployment and lower entry costs.

Features: Teradata provides high scalability, advanced analytics, and support for complex, large-scale data operations. dbt emphasizes SQL-based data transformation, flexibility, and a substantial repository of shared resources and integrations.

Room for Improvement: Teradata could improve in simplifying its deployment process and reducing upfront costs. dbt might enhance its feature set by incorporating more in-depth analytics and offering enterprise-level customer support options.

Ease of Deployment and Customer Service: Teradata deployment tends to be complex, requiring detailed configuration but benefits from comprehensive customer service. dbt offers simpler deployment within cloud environments, supported by active community input and guidance.

Pricing and ROI: Teradata's pricing involves significant upfront investment, reflecting its high-end capabilities and aiming for substantial ROI. dbt presents a cost-effective entry point, appealing to teams leveraging cloud-based environments for transformation and providing valuable returns on smaller budgets.

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

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Teradata vs. dbt

December 2025

Helped 881,082 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 Integration

27th

Average Rating

7.8

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Data Quality (17th)

Teradata

Ranking in Data Integration

15th

Average Rating

8.2

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Customer Experience Management (6th), Backup and Recovery (15th), Relational Databases Tools (6th), Data Warehouse (3rd), BI (Business Intelligence) Tools (8th), Marketing Management (5th), Cloud Data Warehouse (3rd), Database Management Systems (DBMS) (3rd)

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 Teradata is 0.9%, up from 0.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Market Share Distribution
Product	Market Share (%)
Teradata	0.9%
dbt	1.7%
Other	97.4%

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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David Durand Velásquez

Engineers at a consultancy with 11-50 employees

Delivers consistent performance and enables advanced analytics across complex data environments

Teradata stands out as a solid platform for managing and analyzing large volumes of data. Its architecture allows information to be processed efficiently while maintaining stable performance, even in highly demanding environments. One of its most notable strengths is the ability to run complex queries at high speed, which is essential for organizations that require timely and reliable analytics. Teradata offers a well-integrated ecosystem that supports working with different types of data and enables scalability as organizational needs grow. Its focus on advanced analytics, integration with modern business intelligence tools, and the ability to operate both on-premise and in the cloud make it a versatile solution for data warehousing and large-scale processing. Teradata's stability, technological maturity, and the availability of strong documentation and best practices are noteworthy. I consider Teradata to be a tool with great potential for any organization looking to enhance its analytical capabilities, optimize data processing, and move toward more data-driven decision-making. Teradata stands out as a solid platform for managing a large volume of data in different projects. Its architecture allows information to be processed efficiently while maintaining stable performance, even in high-demanding environments. A well-integrated AI ecosystem that supports working with different types of data and enables scalability as organizational needs grow across different kinds of enterprises or organizations. The focus on advanced analytics integration with modern business intelligence tools is particularly valuable. Teradata combines a powerful parallel process and optimizing SQL engine with a highly scalable architecture allowing businesses to execute complex queries and analytics in real-time. It supports multi-cloud, hybrid, and on-premise environments, giving organizations flexibility to choose the setup that best aligns with their strategy. One of the biggest strengths is the ability to unify disparate data sources and support high concurrency, enabling different teams, such as analytics, operations, BI, and data science, to access consistent, trusted data across the enterprise.

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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

"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."

"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."

"Improved performance of ETL procedures, reporting."

"The most valuable features are the large volume of data and the structuring of the data to optimize it and get very optimal data warehouse solutions for customers."

"The product is reliable."

"It's a pre-configured appliance that requires very little in terms of setting-up."

"The key advantages are Performance when processing Terabytes of data and scalability."

"Auto-partitioning and indexing, and resource allocation on the fly are key features."

"Building a data warehouse with Teradata has definitely helped a lot of our downstream applications to more easily access information."

"It is quick, secure, and has less hassles because we don't have to involve our networking team, infrastructure, etc. It is very easy to deploy and make market ready."

More Teradata pros

Cons

"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."

"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."

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

"The tool's flexibility and capacity for expansion are areas of concern where improvements are required."

"The scalability could be better. The on-premises solution is always more complicated to scale."

"Azure Synapse SQL has evolved from a solely dedicated support tool to a data lake. It can store data from multiple systems, not just traditional database management systems. On the other hand, Teradata has limitations in loading flat files or unstructured data directly into its warehouse. In Azure Synapse SQL, we can implement machine learning using Python scripts. Additionally, Azure Synapse SQL offers advanced analytical capabilities compared to Teradata. Teradata is also expensive."

"The capability to implement it with comparable performance across various private cloud environments, ensuring adaptability to different infrastructure setups would be beneficial."

"Teradata is somewhat late in adopting cloud technology."

"Teradata's UI could be more user-friendly."

"Teradata is an old data warehouse, and they're not improving in terms of new, innovative features."

"The current operational approach needs improvement."

More Teradata cons

Pricing and Cost Advice

"The solution’s pricing is affordable."

"The price of Teradata could be less expensive."

"The cost is substantial, totaling around $1.2 million, solely dedicated to upgrading the hardware."

"The solution requires a license."

"In this day and age, we want to get things done quickly. So, we go to the AWS Marketplace."

"Users have to pay a yearly licensing fee for Teradata IntelliFlex, which is very expensive."

"The price needs to be more competitive as Hadoop, Redshift, Snowflake, etc are constantly making way into EDW space."

"The price of the solution could be reduced, it is expensive."

"It is still a very expensive solution. While I very much like the pure technological supremacy of the software itself, I believe Teradata as a company needs to become more affordable. They are already losing the market to more flexible or cheaper competitors."

More Teradata pricing and cost advice

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Use our free recommendation engine to learn which Data Integration solutions are best for your needs.

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Comparison Review

it_user232068

Senior Data Architect at a pharma/biotech company with 1,001-5,000 employees

Aug 5, 2015

Netezza vs. Teradata

Original published at https://www.linkedin.com/pulse/should-i-choose-net Two leading Massively Parallel Processing (MPP) architectures for Data Warehousing (DW) are IBM PureData System for Analytics (formerly Netezza) and Teradata. I thought talking about the similarities and differences…

Read full review

Top Industries

By visitors reading reviews

Financial Services Firm

14%

Insurance Company

Manufacturing Company

Computer Software Company

Financial Services Firm

24%

Manufacturing Company

Computer Software Company

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	13
Large Enterprise	52

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.

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...

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...

Comparing Teradata and Oracle Database, which product do you think is better and why?

I have spoken to my colleagues about this comparison and in our collective opinion, the reason why some people may declare Teradata better than Oracle is the pricing. Both solutions are quite simi...

Which companies use Teradata and who is it most suitable for?

Before my organization implemented this solution, we researched which big brands were using Teradata, so we knew if it would be compatible with our field. According to the product's site, the comp...

Is Teradata a difficult solution to work with?

Teradata is not a difficult product to work with, especially since they offer you technical support at all levels if you just ask. There are some features that may cause difficulties - for example,...

Oracle Data Integrator (ODI) vs dbt

Comparisons

SSIS vs dbt

Compared 20% of the time

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

Tobiko vs dbt

Compared 7% of the time

More dbt Competitors

Oracle Exadata vs Teradata

Compared 7% of the time

Snowflake vs Teradata

Compared 6% of the time

Oracle Database vs Teradata

Compared 5% of the time

SQL Server vs Teradata

Compared 5% of the time

IBM Db2 Database vs Teradata

Compared 5% of the time

More Teradata Competitors

Product Reports

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Data Integration

January 2026

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January 2026

Also Known As

No data available

IntelliFlex, Aster Data Map Reduce, , QueryGrid, Customer Interaction Manager, Digital Marketing Center, Data Mover, Data Stream Architecture, Teradata Vantage Enterprise (DIY)

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

Teradata is a powerful tool for handling substantial data volumes with its parallel processing architecture, supporting both cloud and on-premise environments efficiently. It offers impressive capabilities for fast query processing, data integration, and real-time reporting, making it suitable for diverse industrial applications.

Known for its robust parallel processing capabilities, Teradata effectively manages large datasets and provides adaptable deployment across cloud and on-premise setups. It enhances performance and scalability with features like advanced query tuning, workload management, and strong security. Users appreciate its ease of use and automation features which support real-time data reporting. The optimizer and intelligent partitioning help improve query speed and efficiency, while multi-temperature data management optimizes data handling.

What are the key features of Teradata?

Parallel Processing Architecture: Enables efficient handling of substantial data volumes.
Cloud and On-Premise Deployment: Offers seamless adaptability across environments.
Query Tuning: Improves speed and performance of data retrieval processes.
Workload Management: Ensures optimal resource allocation for enhanced performance.
Robust Security: Provides high levels of data protection and system integrity.
Data Integration: Facilitates cohesive data management from multiple sources.
Automation for Real-time Reporting: Enhances timely data insights and decision-making.
Optimizer and Intelligent Partitioning: Further increase query speed and system efficiency.
Multi-Temperature Data Management: Optimizes the handling and retrieval of data.

What benefits and ROI do users look for?

Efficient Data Management: Facilitates smooth integration and processing of diverse data sets.
Scalability: Easy adjustment to increasing or decreasing data requirements.
Improved Performance: Fast query speeds and optimized resource use.
Comprehensive Security: Safeguards data integrity and accessibility.
Ease of Use: Simplifies data handling and operational processes.

In the finance, retail, and government sectors, Teradata is employed for data warehousing, business intelligence, and analytical processing. It handles vast datasets for activities like customer behavior modeling and enterprise data integration. Supporting efficient reporting and analytics, Teradata enhances data storage and processing, whether deployed on-premise or on cloud platforms.

Sample Customers

Information Not Available

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