IBM Db2 Warehouse on Cloud vs Teradata comparison

IBM Db2 Warehouse on Cloud

Read 76 Teradata reviews

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Executive SummaryUpdated on Apr 20, 2025

Teradata and IBM Db2 Warehouse on Cloud are competing in the data warehousing space. Teradata leads in scalability and analytics, while IBM Db2 Warehouse on Cloud excels in integration and deployment.

Features: Teradata offers high scalability, robust analytics, and real-time processing. It handles complex queries efficiently. IBM Db2 Warehouse on Cloud provides strong integration, versatile deployment, and machine learning capabilities. Its strength lies in intelligent integration and AI.

Room for Improvement: Teradata could enhance its integration features, simplify its user interface, and improve support for more streamlined query optimization. IBM Db2 Warehouse on Cloud may benefit from improving analytics depth, expanding scalability options, and enhancing real-time query handling capabilities.

Ease of Deployment and Customer Service: IBM Db2 Warehouse on Cloud offers straightforward deployment with flexible scaling and excellent customer support. Teradata focuses on performance tuning with an efficient, though less flexible, deployment process.

Pricing and ROI: Teradata involves higher setup costs but offers significant ROI for data-intensive operations due to its performance efficiency. IBM Db2 Warehouse on Cloud provides competitive pricing with cloud-specific benefits, leading to faster ROI realization.

To learn more, read our detailed IBM Db2 Warehouse on Cloud vs. Teradata Report (Updated: April 2025).

IBM Db2 Warehouse on Cloud vs. Teradata

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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

IBM Db2 Warehouse on Cloud

Ranking in Cloud Data Warehouse

16th

Average Rating

7.6

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Teradata

Ranking in Cloud Data Warehouse

6th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Customer Experience Management (6th), Backup and Recovery (20th), Data Integration (17th), Relational Databases Tools (7th), Data Warehouse (3rd), BI (Business Intelligence) Tools (10th), Marketing Management (6th)

Mindshare comparison

As of May 2025, in the Cloud Data Warehouse category, the mindshare of IBM Db2 Warehouse on Cloud is 0.5%, up from 0.3% compared to the previous year. The mindshare of Teradata is 8.7%, down from 9.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse

Featured Reviews

FarhanFarhan

Database Engineer at Meezan Bank

Enhancing analytics with seamless data dumping and reliable support

Our primary use case is data storage and analytics The organization has decided to purchase a full stack solution from IBM due to positive responses, which helped them upgrade from the previous version. The data dumping into the raw zone and the feature of BigQuery is quite attractive. There…

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SurjitChoudhury

Data engineer at Cocos pt

Offers seamless integration capabilities and performance optimization features, including extensive indexing and advanced tuning capabilities

We created and constructed the warehouse. We used multiple loading processes like MultiLoad, FastLoad, and Teradata Pump. But those are loading processes, and Teradata is a powerful tool because if we consider older technologies, its architecture with nodes, virtual processes, and nodes is a unique concept. Later, other technologies like Informatica also adopted the concept of nodes from Informatica PowerCenter version 7.x. Previously, it was a client-server architecture, but later, it changed to the nodes concept. Like, we can have the database available 24/7, 365 days. If one node fails, other nodes can take care of it. Informatica adopted all those concepts when it changed its architecture. Even Oracle databases have since adapted their architecture to them. However, this particular Teradata company initially started with its own different type of architecture, which major companies later adopted. It has grown now, but initially, whatever query we sent it would be mapped into a particular component. After that, it goes to the virtual processor and down to the disk, where the actual physical data is loaded. So, in between, there's a map, which acts like a data dictionary. It also holds information about each piece of data, where it's loaded, and on which particular virtual processor or node the data resides. Because Teradata comes with a four-node architecture, or however many nodes we choose, the cost is determined by that initially. So, what type of data does each and every node hold? It's a shared-no architecture. So, whatever task is given to a virtual processor it will be processed. If there's a failure, then it will be taken care of by another virtual processor. Moreover, this solution has impacted the query time and data performance. In Teradata, there's a lot of joining, partitioning, and indexing of records. There are primary and secondary indexes, hash indexing, and other indexing processes. To improve query performance, we first analyze the query and tune it. If a join needs a secondary index, which plays a major role in filtering records, we might reconstruct that particular table with the secondary index. This tuning involves partitioning and indexing. We use these tools and technologies to fine-tune performance. When it comes to integration, tools like Informatica seamlessly connect with Teradata. We ensure the Teradata database is configured correctly in Informatica, including the proper hostname and properties for the load process. We didn't find any major complexity or issues with integration. But, these technologies are quite old now. With newer big data technologies, we've worked with a four-layer architecture, pulling data from Hadoop Lake to Teradata. We configure Teradata with the appropriate hostname and credentials, and use BTEQ queries to load data. Previously, we converted the data warehouse to a CLD model as per Teradata's standardized procedures, moving from an ETL to an EMT process. This allowed us to perform gap analysis on missing entities based on the model and retrieve them from the source system again. We found Teradata integration straightforward and compatible with other tools.

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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

"The way that it scales will help a lot of customers that are stuck with Netezza boxes that can't grow any larger."

"It will be MPP, so performance should improve."

"The performance is okay as long as the volume of queries is not too high."

"It is stable when there is support from IBM."

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

"We really enjoy the FastLoad, TPump, and MultiLoad features."

"Teradata's pretty fast."

"It handles large amounts of information with a linear performance increase, in relation to a HW investment."

"The data processing, clustering, and distributed computing are impressive."

"The solution's banking model, called FSLDM (Financial Services Logical Data Model), is sophisticated and good."

"Teradata has good performance, the response times are very fast. Overall the solution is easy to use. When we do the transformation, we have all of our staging and aggregation data available."

"The feature that we find most valuable is its ability to perform Massive Parallel Processing."

More Teradata pros

Cons

"Containers get corrupted very easily. Restoring them using GPFS can result in a lot of issues."

"There are some limitations in adding data files to table spaces, and improvements are needed for regional support."

"Ultimately, the product itself has challenges and we are not currently satisfied with the support, either."

"Right now, we are implementing on ESX VMware 6.0. Support for this platform is poor. Also, one of the backup/recovery options is broken and IBM is not addressing the issue."

"Tech support for dashDB is awful. We usually have tickets open for three to four weeks."

"I would like to see an improved Knowledge Base on the web."

"The only issue our company has with Teradata IntelliFlex is that it is not cost-effective because of the way the product has been designed."

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

"Apart from Control-M, it would be nice if it could integrate with other tools."

"I would like more security and speed."

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

"Teradata could improve by being less complicated. There are some aspects that are not available on the Unix server and a Unix system is required to access some data, such as in case of an emergency."

"The cost of Teradata Cloud Data Warehouse has room for improvement."

More Teradata cons

Pricing and Cost Advice

"If your going to go with warehouse DB/dashDB, use the cloud or Sailfish version."

"I rate the product price a nine on a scale of one to ten, where one is cheap and ten is expensive."

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

"Teradata is expensive but gives value for money, especially if you don't want to move your data to the cloud."

"It comes at a notably high cost for what it offers."

"Teradata used to be expensive, but they have been lowering their prices."

"When looking into implementing this product, pricing is the main issue followed by technical expertise."

"Teradata pricing is fine, and it's competitive with all the legacy models. On a scale of one to five, with one being the worst and five being the best, I'm giving Teradata a three, because it can be a little expensive, when compared to other solutions."

"Teradata's licensing is on the expensive side."

More Teradata pricing and cost advice

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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…

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

26%

Computer Software Company

11%

Healthcare Company

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What advice do you have for others considering IBM Db2 Warehouse on Cloud?

Organizations of all sizes, especially those who are in need of powerful and elastic cloud data warehouse solutions that can help administrators maximize the efficiency of their data-based operatio...

What needs improvement with IBM Db2 Warehouse on Cloud?

There are some limitations in adding data files to table spaces, and improvements are needed for regional support.

What is your primary use case for IBM Db2 Warehouse on Cloud?

Our primary use case is data storage and analytics.

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 Exadata vs IBM Db2 Warehouse on Cloud

Comparisons

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Compared 32% of the time

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Compared 14% of the time

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Compared 11% of the time

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Compared 7% of the time

MySQL vs Teradata

Compared 7% of the time

IBM Db2 Database vs Teradata

Compared 6% of the time

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Product Reports

Cloud Data Warehouse

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Also Known As

IBM dashDB

IntelliFlex, Aster Data Map Reduce, , QueryGrid, Customer Interaction Manager, Digital Marketing Center, Data Mover, Data Stream Architecture

Overview

IBM dashDB family offers private and public cloud database solutions for transactional and analytic workloads, with IBM fully managed or client managed options with a Common SQL engine across all deployment options.

IBM

Teradata is a scalable data analytics platform designed to meet enterprise demands for large-scale data management and processing, focusing on performance, scalability, and security for complex query executions.

As a leading data warehousing solution, Teradata integrates advanced analytics enabling organizations to derive insights from massive datasets. It supports high-volume data workloads with its architecture optimized for analytical queries. Users benefit from its robust scalability, allowing seamless expansion as data grows. Teradata's SQL engine is compatible with a wide range of data types, ensuring flexibility in data analysis. With advanced security measures, it protects sensitive data across various environments, providing peace of mind to users handling critical information.

What are the most important features of Teradata?

Linear Scalability: Easily expands data processing capabilities to handle increasing data loads without performance degradation.
Advanced Analytics: Supports complex analytical operations integrating machine learning and AI for predictive insights.
SQL Compatibility: Offers a comprehensive SQL interface that supports complex queries across diverse data types.
Data Security: Implements rigorous security protocols to protect data integrity and confidentiality.

What benefits and ROI should users look for?

Improved Decision-Making: Derive actionable insights quickly for strategic business decisions.
Cost Efficiency: Optimized resource usage reduces operational costs associated with data processing.
Increased Productivity: Streamlined data operations save time, allowing teams to focus on analysis.
Enhanced Scalability: Seamless growth without significant additional investment as data volumes increase.

Teradata is widely used in industries like finance, telecommunications, and healthcare, where data-driven decisions are critical. Companies leverage its robust analytics capabilities to enhance customer experiences, streamline operations, and ensure compliance with regulatory requirements. In these sectors, quick access to data insights can significantly impact competitive advantage.

Sample Customers

Copenhagen Business School, BPM Northwest, GameStop

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IBM Db2 Warehouse on Cloud vs. Teradata