

Teradata and IBM Cloud Pak for Data compete in the data management and analytics space. IBM Cloud Pak for Data seems to have an upper hand due to its superior integration abilities and support for hybrid and multi-cloud environments, while Teradata is noted for performance and stability.
Features: Teradata delivers fast query execution and parallel processing, supporting large-scale data management tasks efficiently. It is known for its reliability and scalability, making it suitable for extensive data warehousing needs. IBM Cloud Pak for Data excels with its integration capabilities, allowing seamless data governance through Watson Knowledge Catalog. It offers a comprehensive suite for data virtualization and AI, accommodating hybrid and multi-cloud environments.
Room for Improvement: Teradata could enhance its handling of unstructured data and improve its cloud offerings and AI integration. There is also a call for improved replication features and a more competitive cost structure. IBM Cloud Pak for Data requires better resource utilization and performance consistency, particularly in cloud settings. Simplifying integration and setup processes would help in broader adoption.
Ease of Deployment and Customer Service: Teradata provides versatile deployment options across different environments, with praised technical support noted for its responsiveness and expertise. IBM Cloud Pak for Data effectively supports hybrid cloud environments, though it receives mixed reviews on technical support and response times, with deployment complexity sometimes seen as a barrier.
Pricing and ROI: Teradata is considered pricey, suited for larger enterprises needing advanced analytics, though it promises significant ROI through performance improvements. IBM Cloud Pak for Data is also costly, especially for smaller companies, with less flexible pricing models than some users expect. Both products emphasize the need for greater price competitiveness to attract a wider market.
We have been able to drive responsible, transparent, and explainable AI workflow to operationalize AI and mitigate risk and regulatory compliance easily.
It is easy to collect, organize, and analyze data no matter where it is, hence being able to make data-driven decisions.
At least fifteen to twenty percent of our time has been saved using Teradata, which has positively affected team productivity and business outcomes.
Independent research showed that Teradata VantageCloud users achieved an average ROI of 427% across three years with payback under a year, demonstrating the platform's ability to deliver a strong financial return.
We have realized a return on investment, with a reduction of staff from 27 to eight, and our current return on investment is approximately 14%.
Cloud Pak is a complicated system, and it's often difficult to find the right resource in IBM to help with specific issues.
The customer support for IBM Cloud Pak for Data is great and responsive.
The response time for IBM's technical support is excellent.
The customer support for Teradata has been great.
They are responsive and knowledgeable, and the documentation is very helpful.
Customer support is very good, rated eight out of ten under our essential agreement.
I have not noticed any downtime or lagging, especially when dealing with large data, so it is relatively very scalable.
IBM Cloud Pak for Data's scalability is very good; it can be used by any size of organization.
Whenever we need more resources, we can add that in Teradata, and when not needed, we can scale it down as well.
This flexibility allows organizations to scale according to their needs, balancing performance, cost, and compliance requirements.
This expansion can occur without incurring downtime or taking systems offline.
The overall performance of IBM Cloud Pak for Data, particularly with IBM DataStage for ETL processes, is very good.
Its massively parallel process architecture allows the platform to distribute workload efficiently, enabling organizations to run heavy analytic queries without compromising speed or stability.
I find the stability to be almost a ten out of ten.
The workload management and software maturity provide a reliable system.
Setting up the hybrid and multi-cloud environments is a long job and it takes time.
IBM Cloud Pak for Data can be improved because processing speeds are sometimes slow.
To improve IBM Cloud Pak for Data, I suggest more out-of-the-box integration.
I want to highlight two features for improvement: first, storing data in various formats without requiring a tabular structure, accommodating unstructured data; and second, adding AI ML features to better integrate Gen AI, LLM concepts, and user-friendly experiences such as text-to-SQL capabilities.
Unlike SQL and Oracle, which have in-built replication capabilities, we don't have similar functionality with Teradata.
The most challenging aspect is finding Teradata resources, so we are focusing on internal training and looking for more Teradata experts.
The setup cost is very expensive.
Regarding my experience with pricing, setup cost, and licensing, for a small organization, the price might be relatively high, but for huge enterprises such as ours, the price is relatively affordable.
The list price is high, but the flexibility in pricing is adequate.
Teradata is much more expensive than SQL, which is well-performed and cheaper.
Initially, it may seem expensive compared to similar cloud databases, however, it offers significant value in performance, stability, and overall output once in use.
Role-based access control (RBAC), strong audit and compliance features, high availability, fault tolerance, and encrypted data at rest and in-transit are key features.
From there, I can work my way into a more granular level, applying all of that information on top of my actual data to understand what my data looks like, where it came from, and where it went wrong, managing it throughout the cycle.
The benefits of choosing IBM Cognos, in addition to saving on cost, include having institutional knowledge about maintaining this infrastructure and enough people who have developed on Cognos in the past, which creates comfort in its use.
We have been able to save approximately 80 percent of our time. We are not doing data analysis manually, so this relieves our data department of dealing with data.
Teradata's security helps our organization meet compliance requirements such as GDPR and IFRS, and it is particularly essential for revenue contracting or revenue recognition.
Its architecture allows information to be processed efficiently while maintaining stable performance, even in highly demanding environments.
It facilitates data integration, where we integrate and analyze data from various sources, making it a powerful and high-quality reliable solution for the company.
| Product | Mindshare (%) |
|---|---|
| Teradata | 1.0% |
| IBM Cloud Pak for Data | 1.2% |
| Other | 97.8% |


| Company Size | Count |
|---|---|
| Small Business | 9 |
| Large Enterprise | 15 |
| Company Size | Count |
|---|---|
| Small Business | 28 |
| Midsize Enterprise | 13 |
| Large Enterprise | 52 |
IBM Cloud Pak for Data is a comprehensive platform integrating data management, AI, and machine learning capabilities tailored for hybrid environments. It's renowned for enhancing productivity through efficient data analytics and management.
This platform offers data virtualization, robust analytics, and AI-driven processes. Its integration capabilities, including IBM MQ and App Connect, facilitate seamless data connections. Users benefit from containerization, data governance, and compatibility with hybrid systems, improving decision-making and management productivity. However, the requirement of extensive infrastructure and performance challenges can impact scalability for small businesses.
What are the key features of IBM Cloud Pak for Data?In the financial and banking sectors, IBM Cloud Pak for Data is utilized for data management tasks like spend analytics and contract leakage analysis. It's used for data integration, machine learning, and AI-driven analytics to transform data into valuable insights in industries such as FinTech and consultancy.
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?
What benefits and ROI do users look for?
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.
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