IBM Cloud Pak for Data vs Teradata comparison

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IBM Cloud Pak for Data

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Executive SummaryUpdated on Jul 27, 2025

Teradata and IBM Cloud Pak for Data compete in the data management and analytics space. Teradata seems to have the upper hand in managing large datasets with high reliability due to its adaptability and efficient parallel processing.

Features: Teradata stands out for its adaptability and efficient parallel processing capabilities, enabling robust data warehousing solutions. Its scalability makes it suitable for real-time operational reporting and managing large datasets. IBM Cloud Pak for Data showcases a powerful suite of tools like Watson Studio, Machine Learning, and seamless integration. Its focus on data governance and containerization enhances its versatility for data preparation and management.

Room for Improvement: Teradata could improve its transaction-level processing and pricing models, aiming for better cloud integration and user-friendliness. Enhancing these areas could secure its market position against emerging solutions. IBM Cloud Pak for Data requires improvements in infrastructure requirements and cost efficiency. Smoother cloud transitions and enhanced native feature support could significantly boost its competitive edge.

Ease of Deployment and Customer Service: Teradata is primarily deployed on-premises, praised for excellent technical support available to users, although deployment can be complex due to legacy system intricacies. IBM Cloud Pak for Data offers flexible deployment options across public and hybrid clouds. Its customer service is well-regarded for accuracy, although initial complexities in setup might deter smaller clients.

Pricing and ROI: Teradata, positioned as a premium solution, justifies its costs with high performance and reliability, offering positive ROI among large enterprises, though cost may be challenging for smaller businesses. IBM Cloud Pak for Data offers competitive pricing but remains expensive for smaller enterprises. Users appreciate the ROI from enhanced analytic capabilities and integration, while price adjustments are suggested for broader market appeal.

To learn more, read our detailed IBM Cloud Pak for Data vs. Teradata Report (Updated: July 2025).

IBM Cloud Pak for Data vs. Teradata

July 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

IBM Cloud Pak for Data

Ranking in Data Integration

24th

Average Rating

7.8

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Data Virtualization (3rd)

Teradata

Ranking in Data Integration

15th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Customer Experience Management (6th), Backup and Recovery (20th), Relational Databases Tools (8th), Data Warehouse (3rd), BI (Business Intelligence) Tools (9th), Marketing Management (6th), Cloud Data Warehouse (4th)

Mindshare comparison

As of August 2025, in the Data Integration category, the mindshare of IBM Cloud Pak for Data is 2.0%, up from 1.7% compared to the previous year. The mindshare of Teradata is 1.2%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration

Featured Reviews

Michelle Leslie

Data asset management engineer at a tech services company with 1-10 employees

Starts strong with data management capabilities but needs a demo database

What I would love to see is an end-to-end, almost a training demo database of some sort, where one of the biggest problems with data management is demonstrated. There are so many components to data management, and more often than not, people understand one thing really well. They may understand DataStage and how to move data around, but they do not see the impact of moving data incorrectly. They also do not see the impact of everyone understanding a piece of data in the same way. I would love Cloud Pak to come with a demo database that illustrates the different components of data management in a logical way, so I can see the whole picture instead of just the area I'm specializing in. It would be great if Cloud Pak, from a data modeling point of view, allowed us to import our PDMs, for example. It would be ideal to import and create business terms in Cloud Pak. The PEA would be great to create the technical data. The association between the business and the technical metadata could then be automated by pulling it through from your ACE models. The data modeling component is available in Cloud Pak. Additionally, when it comes to Cloud Pak, even though it has the NextGen DataStage built into it, there is Cloud Pak for data integration as well. Currently, I do not think we have a full enough understanding of how CP4D and CP4I can enhance each other.

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RajeshKumar25

Principal Consultant & Director at Quensulting

Harnessing advanced parallelism for top performance while embracing cloud trends

Teradata is primarily used for data warehousing across all customers. My clients have built-in applications that use Teradata, and their use varies from customer to customer, depending on the industry and database size. The primary function is as an OLAP analytical ecosystem Scalability is…

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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

"Cloud Pak is a very, very, very good system."

"I love the way that I can start at a very basic level with my data management journey by capturing my policies, justifying my data, and putting them into different categories to say this is data relating to individuals, for example, or data relating to geography."

"DataStage allows me to connect to different data sources."

"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."

"You can model the data there, connect the data models with the business processes and create data lineage processes."

"What I found most helpful in IBM Cloud Pak for Data is containerization, which means it's easy to shift and leave in terms of moving to other clouds. That's an advantage of IBM Cloud Pak for Data."

"Cloud Pak's most valuable features are IBM MQ, IBM App Connect, IBM API Connect, and ISPF."

"IBM Watson Catalog and data pipelines are the most valuable features of the solution."

More IBM Cloud Pak for Data pros

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

"The ease of deployment is useful so clients are up and running quickly in comparison to other products."

"It has given our business the ability to gain insights into the data and create data labs for analysis and PoCs."

"The performance is great, we are able to query our data in one operation."

"It's the same as your visual database. I like the fast load feature for data, the BTQ solution is very good, and storage procedures are very fast."

"It's stable and reliable."

"It is a highly robust software solution."

"The most valuable features of Teradata are that it is a massively parallel platform and I can receive a lot of data and get the queries out correctly, especially if it's been appropriately designed. The native features make it very suitable for multiple large data tasks in a structured data environment. Additionally, the automation is very good."

More Teradata pros

Cons

"There is a solution that is part of IBM Cloud Pak for Data called Watson OpenScale. It is used to monitor the deployed models for the quality and fairness of the results. This is one area that needs a lot of improvement."

"The solution could have more connectors."

"The setup cost is very expensive. The cost depends on the pieces of the solution I'm using, how much data I have, and whether it's on the cloud or on-prem."

"The tool depends on the control plane, an OpenShift container platform utilized as an orchestration layer...So, we have communicated this issue to IBM and asked if it is feasible to adapt the solution to work on a Kubernetes platform that we support."

"What I would love to see is an end-to-end, almost a training demo database of some sort, where one of the biggest problems with data management is demonstrated."

"One challenge I'm facing with IBM Cloud Pak for Data is native features have been decommissioned, such as XML input and output. Too many changes have been made, and my company has around one hundred thousand mappings, so my team has been putting more effort into alternative ways to do things. Another area for improvement in IBM Cloud Pak for Data is that it's more complicated to shift from on-premise to the cloud. Other vendors provide secure agents that easily connect with your existing setup. Still, with IBM Cloud Pak for Data, you have to perform connection migration steps, upgrade to the latest version, etc., which makes it more complicated, especially as my company has XML-based mappings. Still, the XML input and output capabilities of IBM Cloud Pak for Data have been discontinued, so I'd like IBM to bring that back."

"The solution's user experience is an area that has room for improvement."

"The interface could improve because sometimes it becomes slow. Sometimes there is a delay between clicks when using the software, which can make the development process slow. It can take a few seconds to complete one action, and then a few more seconds to do the next one."

More IBM Cloud Pak for Data cons

"They should add more connectors to different platforms."

"The increasing volumes of data demand more and more performance."

"I would like to see more integration with many different types of data."

"The SQL Assistant is very basic. This tool can be improved for usability."

"The current operational approach needs improvement."

"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."

"Stability-wise, we have had some issues with automation and the ability to handle large datasets."

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

More Teradata cons

Pricing and Cost Advice

"Cloud Pak's cost is a little high."

"I think that this product is too expensive for smaller companies."

"The solution's pricing is competitive with that of other vendors."

"It's quite expensive."

"IBM Cloud Pak for Data is expensive. If we include the training time and the machine learning, it's expensive. The cost of the execution is more reasonable."

"I don't have the exact licensing cost for IBM Cloud Pak for Data, as my company is still finalizing requirements, including monthly, yearly, and three-year licensing fees. Still, on a scale of one to five, I'd rate it a three because, compared to other vendors, it's more complicated."

"The solution is expensive."

"For the licensing of the solution, there is a yearly payment that needs to be made. Also, since it is expensive, cost-wise, I rate the solution an eight or nine out of ten."

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

"The tool costs about 30,000 euros a month, while Azure Synapse SQL only costs 10,000."

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

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

"Make sure you have the in-house skills to design and support the solution, as relying on external sources is extremely costly and tends to lock you into specific platforms, tools, and paradigms."

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

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

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

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

Financial Services Firm

30%

Manufacturing Company

11%

Computer Software Company

Government

Financial Services Firm

26%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about IBM Cloud Pak for Data?

DataStage allows me to connect to different data sources.

What is your experience regarding pricing and costs for IBM Cloud Pak for Data?

The setup cost is very expensive. The cost depends on the pieces of the solution I'm using, how much data I have, and whether it's on the cloud or on-prem.

What needs improvement with IBM Cloud Pak for Data?

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,...

IBM InfoSphere DataStage vs IBM Cloud Pak for Data

Comparisons

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Compared 12% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs IBM Cloud Pak for Data

Compared 10% of the time

Azure Data Factory vs IBM Cloud Pak for Data

Compared 9% of the time

Palantir Foundry vs IBM Cloud Pak for Data

Compared 8% of the time

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Oracle Exadata vs Teradata

Compared 12% of the time

SQL Server vs Teradata

Compared 8% of the time

IBM Db2 Database vs Teradata

Compared 7% of the time

Snowflake vs Teradata

Compared 7% of the time

OpenText Analytics Database (Vertica) vs Teradata

Compared 6% of the time

More Teradata Competitors

Product Reports

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IBM Cloud Pak for Data

August 2025

Download Teradata product report

July 2025

Also Known As

Cloud Pak for Data

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

Overview

IBM Cloud Pak® for Data is a fully-integrated data and AI platform that modernizes how businesses collect, organize and analyze data to infuse AI throughout their organizations. Cloud-native by design, the platform unifies market-leading services spanning the entire analytics lifecycle. From data management, DataOps, governance, business analytics and automated AI, IBM Cloud Pak for Data helps eliminate the need for costly, and often competing, point solutions while providing the information architecture you need to implement AI successfully.

Building on the streamlined hybrid-cloud foundation of Red Hat® OpenShift®, IBM Cloud Pak for Data takes advantage of the underlying resource and infrastructure optimization and management. The solution fully supports multicloud environments such as Amazon Web Services (AWS), Azure, Google Cloud, IBM Cloud™ and private cloud deployments. Find out how IBM Cloud Pak for Data can lower your total cost of ownership and accelerate innovation.

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

Qatar Development Bank, GuideWell, Skanderborg Music Festival

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