IBM Cloud Pak for Data vs Teradata comparison

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IBM Cloud Pak for Data

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Executive SummaryUpdated on Apr 20, 2025

Teradata and IBM Cloud Pak for Data compete in the data warehousing and analytics space. Based on features, Teradata has the upper hand with its advanced data processing capabilities and integration, while IBM excels in AI integration and data governance.

Features: Teradata offers high-speed and scalable architecture with powerful parallel processing, advanced workload management, and comprehensive adaptability, making it a top choice for large-scale data processing. Its robust data integration and handling capabilities lead the data warehousing market. IBM Cloud Pak for Data stands out with strong data virtualization, seamless AI integration, and a focus on data governance, appealing to organizations emphasizing innovative analytics solutions.

Room for Improvement: Teradata could enhance its offerings with better cloud services, increased cost-effectiveness, and expanded support for unstructured data. Additionally, improved integration with big data platforms and more sophisticated reporting tools are needed. IBM Cloud Pak for Data users suggest improving installation processes, performance optimization, and enhancing connector integration for diversified platforms.

Ease of Deployment and Customer Service: Teradata is recognized for its effective presence in on-premises and hybrid cloud environments, coupled with comprehensive customer service, though some users report slower response times. IBM Cloud Pak for Data offers flexibility as a cloud-centric solution but faces challenges in initial deployment ease and infrastructure demands. Both products provide robust support systems, with Teradata's customer service often seen as more specialized.

Pricing and ROI: Teradata is viewed as expensive but offers superior data handling and reliability, often delivering a solid return on investment in large deployments. IBM Cloud Pak for Data's pricing appears costly, especially for small enterprises, despite offering cost-competitive features. Both emphasize flexible pricing strategies, though IBM's high initial costs may deter smaller businesses.

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

IBM Cloud Pak for Data vs. Teradata

June 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 (5th), 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 June 2025, in the Data Integration category, the mindshare of IBM Cloud Pak for Data is 1.9%, up from 1.7% compared to the previous year. The mindshare of Teradata is 1.1%, 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

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

"Scalability-wise, I rate the solution a nine or ten out of ten."

"It is a scalable solution, and we have had no issues with its scalability in our company. I rate the solution's scalability a nine out of ten."

"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."

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

"The most valuable features of IBM Cloud Pak for Data are the Watson Studio, where we can initiate more groups and write code. Additionally, Watson Machine Learning is available with many other services, such as APIs which you can plug the machine learning models."

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

"Its data preparation capabilities are highly valuable."

More IBM Cloud Pak for Data pros

"In Data Lab, you can schedule any testing you want to do in production. You can take a small subset of data from production, copy it there, and run all your tests. It reduces your testing costs because it's all in the lab."

"The most valuable feature is the ease of uploading data from multiple sources."

"The product is reliable."

"Things have started moving faster in my company, such as data retrieval happens more quickly."

"I've never had any issues with scalability."

"The cloud is ten times better than physical hardware; it is more cost-effective and the upgrade process is ten times easier."

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

"The ability to handle machine data parallel processing is the most valuable feature of Teradata."

More Teradata pros

Cons

"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."

"One thing that bugs me is how much infrastructure Cloud Pak requires for the initial deployment. It doesn't allow you to start small. The smallest permitted deployment is too big. It's a huge problem that prevents us from implementing the solution in many scenarios."

"Cloud Pak would be improved with integration with cloud service providers like Cloudera."

"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 technical support could be a little better."

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

"The solution could have more connectors."

"The product is trying to be more maturity in terms of connectors. That, I believe, is an area where Cloud Pak can improve."

More IBM Cloud Pak for Data cons

"Teradata should focus on functionality for building predictive models because, in that regard, it can definitely improve."

"It is hard for some of our users to set up rules for cleansing and transforming data, so this is something that could be improved."

"There is a need to improve performance in high transaction processes, as well as the reporting system."

"I've been using the same UI for 20 years in Teradata. It could use some updating. Adding more stability around Teradata Studio would be outstanding. Teradata Studio is a Java-based version of their tool. It's much better now, but it still has some room for improvement."

"Teradata's pricing is quite high compared to Redshift, Synapse, or GCP alternatives."

"Sometimes the large injestion takes days to load data, and some of our stored procedures take two to three days."

"I think the UI is not there yet. It could be improved by being more user-friendly."

"It needs a teaching web site with more training on third-party tools used for BI."

More Teradata cons

Pricing and Cost Advice

"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's quite expensive."

"The solution is expensive."

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

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

"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."

"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."

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

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

"The price of Teradata could be less expensive."

"The price of Teradata is expensive. However, what they deliver they are outstanding. If you're looking for an inexpensive solution to run a database, this isn't your tool. It's the Ferrari of databases for data warehousing."

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

"In the past, it turned out that other solutions, in order to provide the full range of abilities that the Teradata platform provides plus the migration costs, would end up costing more than Teradata does."

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

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

"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

Financial Services Firm

30%

Manufacturing Company

10%

Computer Software Company

10%

Government

Financial Services Firm

26%

Computer Software Company

11%

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

Compared 15% of the time

Denodo vs IBM Cloud Pak for Data

Compared 13% of the time

Azure Data Factory vs IBM Cloud Pak for Data

Compared 10% of the time

Palantir Foundry vs IBM Cloud Pak for Data

Compared 9% of the time

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Oracle Exadata vs Teradata

Compared 13% of the time

SQL Server vs Teradata

Compared 10% of the time

IBM Db2 Database vs Teradata

Compared 7% of the time

Snowflake vs Teradata

Compared 6% of the time

MySQL vs Teradata

Compared 6% of the time

More Teradata Competitors

Product Reports

Download IBM Cloud Pak for Data product report

IBM Cloud Pak for Data

June 2025

Download Teradata product report

May 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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