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IBM Db2 Warehouse on Cloud vs Teradata comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Apr 5, 2026

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
7
Ranking in other categories
No ranking in other categories
Teradata
Ranking in Cloud Data Warehouse
2nd
Average Rating
8.2
Reviews Sentiment
6.9
Number of Reviews
83
Ranking in other categories
Customer Experience Management (5th), Backup and Recovery (10th), Data Integration (14th), Relational Databases Tools (5th), Data Warehouse (2nd), BI (Business Intelligence) Tools (9th), Marketing Management (4th), Database Management Systems (DBMS) (5th)
 

Mindshare comparison

As of September 2026, in the Cloud Data Warehouse category, the mindshare of IBM Db2 Warehouse on Cloud is 2.0%, up from 0.8% compared to the previous year. The mindshare of Teradata is 8.9%, up from 8.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse Mindshare Distribution
ProductMindshare (%)
Teradata8.9%
IBM Db2 Warehouse on Cloud2.0%
Other89.1%
Cloud Data Warehouse
 

Featured Reviews

FM
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…
David Durand Velásquez - PeerSpot reviewer
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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"It will be MPP, so performance should improve."
"Ease of migration from Netezza DB; IBM ported over all Netezza's functionality and made the warehouse DB/dashDB the best of breed of the two."
"The performance is okay as long as the volume of queries is not too high."
"I like that the dashDB solution is built on DB2 technology, which means that you can use all the features of a DB2 database while outsourcing all the hardware and software maintenance."
"It is stable when there is support from IBM."
"DashDB is a good product to work with and the extra cost you spend on performance, technical support and tools to work with is worth it."
"Since my company is an IBM partner, it has enabled us to offer cloud data warehouse solutions on a 100% IBM stack."
"The way that it scales will help a lot of customers that are stuck with Netezza boxes that can't grow any larger.​"
"The solution is speedy and doesn't require much of a DBA team, it's kind of baked right into the product."
"We moved from an Enterprise platform, to non-Enterprise and ended up with a machine that was 20X more powerful."
"Teradata is a 100% stable environment."
"We had few reports which were taking more than 45 minutes, then with Teradata we received results within minutes, and a few under a minute."
"BTEQ is the best feature of Teradata, as it allows me to execute multiple queries such as select, insert, update, and delete in a single script, handle errors, automate running multiple SQL statements, and schedule ETL jobs, making it lightweight and scriptable, unlike SQL Assistant."
"Viewpoint, the detailed query logs and performance statistics are valuable features."
"Teradata is great as far as scalability once you have the product."
"Our enterprise data warehouse (EDW) runs on Teradata and it is the single version of truth for management decision-making and reporting."
 

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."
"The support channels need to improve."
"Ultimately, the product itself has challenges and we are not currently satisfied with the support, either."
"With dashDB, scalability and uptime need more improvement."
"I would like to see improvements in backup and authentication. It needs the ability to increase the number of retained backups to more than 2 days."
"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."
"Db2 is not a solution that I recommend. We have a lot of experience and we are not satisfied with the product or the support that we received."
"Teradata is an expensive tool. Like, if you're already using Microsoft products like Windows, they'll market all their products together. And with the rise of cloud technologies, companies will adopt solutions that offer them some privileges or facilities. Similar to how SAP does it in the market, so do Microsoft and other companies. Even Oracle and other such tools are quite commonly seen compared to Teradata's competitors in everyday solutions."
"Teradata has a few AI models, but in data science, we need more flexibility."
"Data synchronization to the DR site."
"There is a need to improve performance in high transaction processes, as well as the reporting system."
"GUI of administrative tools is really outdated."
"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 limitation we encountered was related to speed, prompting us to increase our AWS cloud thresholds and benchmarks on the servers, adding more throughput."
"My experience was the Teradata Solution Architects were less friendly and hid information and charged for simple things."
 

Pricing and Cost Advice

"If your going to go with warehouse DB/dashDB, use the cloud or Sailfish version."
"Teradata is expensive but gives value for money, especially if you don't want to move your data to the cloud."
"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."
"The price of Teradata could be less expensive."
"​When looking into implementing this product, pricing is the main issue followed by technical expertise​."
"Teradata's licensing is on the expensive side."
"Price is quite high, so if it is really possible to use other solutions (e.g. you do not have strict requirements for performance and huge data volumes), it might be better to look at alternatives from the RDBMS world."
"It comes at a notably high cost for what it offers."
"The initial cost may seem high, but the TCO is low."
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Comparison Review

it_user232068 - PeerSpot reviewer
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…
 

Top Industries

By visitors reading reviews
Financial Services Firm
16%
Comms Service Provider
12%
Outsourcing Company
9%
Construction Company
9%
Financial Services Firm
17%
Outsourcing Company
10%
Manufacturing Company
8%
Construction Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business4
Large Enterprise3
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise13
Large Enterprise53
 

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,...
 

Also Known As

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

Overview

 

Sample Customers

Copenhagen Business School, BPM Northwest, GameStop
Netflix
Find out what your peers are saying about IBM Db2 Warehouse on Cloud vs. Teradata and other solutions. Updated: September 2026.
913,683 professionals have used our research since 2012.