

Teradata and Toad Data Point compete in the data management and analytics tools category. Toad Data Point seems to have the upper hand in cost-effectiveness and cross-database access, though Teradata is stronger in scalability and large data volume handling.
Features: Teradata is known for its scalability, parallel processing, and stable architecture, which ensures efficient handling of massive data volumes with robust security and integration features. Toad Data Point is praised for its wide data connectivity, ease of use with visual query building, and automation capabilities, which facilitate integration from multiple data sources.
Room for Improvement: Teradata users suggest enhancements in handling unstructured data, expanding cloud capabilities, and improving pricing flexibility. Toad Data Point could benefit from a more user-friendly interface and better performance with large data sets.
Ease of Deployment and Customer Service: Teradata offers flexibility with on-premises and cloud environments but shows a preference for on-premises setups. Its customer service is generally good but with variable response times. Toad Data Point provides similar deployment flexibility and receives mixed feedback on its cost-effective support.
Pricing and ROI: Teradata is seen as expensive yet justifiable for large enterprises due to its robust features and high ROI, often referred to as the Ferrari of databases. On the other hand, Toad Data Point is celebrated for being cost-effective across different scales and offers affordable solutions even when some specific licenses come at a higher cost than competitors.
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%.
If they contain duplicate counts or null records or improper data, those records would not be reliable.
Financially, I understand that teams often see a return on investment of one hundred percent plus annually from Toad Data Point through time savings and tool consultation;
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.
The quality of their support is excellent, and the speed is very good, too.
They resolved my issue within a day which was specifically around licensing.
Overall, the service is excellent.
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.
It does not scale well when considering the high cost of the Mac license.
Some aspects, like scalability, could be improved to avoid writing different codes for each database.
Scalability has not been an issue because so far we have dumped about a billion records per year, and I do not see any issues as such.
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.
I often feel instability locally because it is a heavy application, and I feel some slowness in the response of the user interface.
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.
Better data visualization tools, improved integrations with modern tools, and enhanced collaboration features such as shared query libraries and real-time collaborations would be beneficial.
Toad Data Point should include more features for utilizing AI, which can automatically perform many tasks.
The application is heavy on my local PC; however, if I connect to a remote server, I think it works better.
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.
The Mac licenses are expensive, costing 1,600 dollars each.
The pricing for Toad Data Point is where it gets into trouble.
The pricing is cost-effective; it is neither too cheap nor too expensive, it's a good value.
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.
I am able to have cross-connection queries, blend and join data from multiple different databases in a single query, with data profiling, automation and scheduling, and export and reporting tools.
I utilize automations in my database with Ansible automations, performing automation data processing units and deployment, which has a positive impact, increasing efficiency and reducing human error, as well as saving time, thus improving productivity and scalability compared to human errors.
There is a feature called Toad Automation, which is a valuable tool.
| Product | Mindshare (%) |
|---|---|
| Teradata | 1.0% |
| Toad Data Point | 0.8% |
| Other | 98.2% |


| Company Size | Count |
|---|---|
| Small Business | 28 |
| Midsize Enterprise | 13 |
| Large Enterprise | 52 |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 1 |
| Large Enterprise | 5 |
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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