Teradata Reviews

I used this technology for large periods of time during my career. I would highlight Teradata's stability, technological maturity, and the availability of strong documentation and best practices. Overall, 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.

I work in different kinds of organizations, such as government or enterprise, who decided to increase their experience or prepare the best way to process data. During my work, I encountered different cases, and Teradata was the best solution.

Teradata stands out as a solid platform for managing and analyzing 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.

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.

For approximately ten big projects, Teradata integrates modern analytics capabilities, including machine learning, predictive modeling, and advanced data processing, which help organizations move beyond descriptive analytics towards proactive and strategic decision-making.

Teradata stands out as a powerful and reliable platform for transforming enterprise data into meaningful insights, operational efficiency, and strategic value. Teradata is engineered to deliver high performance at scale. Its massively parallel process architecture allows the platform to distribute workload efficiently, enabling organizations to run heavy analytic queries without compromising speed or stability.

Teradata offers strong scalability. Its architecture allows the platform to support different kinds of processes and to expand smoothly as data volumes, users, and workloads grow. In my experience, it handles growth efficiently without disruptions or performance degradation.

Teradata supports hybrid and multi-cloud flexibility. Teradata supports multiple deployment models including on-premise, public cloud, AWS, Azure, GCP, multi-cloud, and hybrid cloud. This flexibility allows organizations to scale according to their needs, balancing performance, cost, and compliance requirements.

Teradata's customer support is solid. They provide helpful guidance and resources when needed.

I would rate Teradata's customer support an 8 out of 10. They are responsive and knowledgeable, and the documentation is very helpful.

Positive

I managed different kinds of technologies and used other tools previously, but Teradata's overall performance and reliability are clearly superior.

In my experience, I can see different scenarios around metrics and different kinds of topics. In my case, Teradata was a great experience for different scenarios. It is necessary to include machine learning, predictive models, and advanced data processing. 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. In my experience, I've seen accelerated analytics and operational benefits aligning with this finding.

The cost was incredible because the efficiency of this technology is very useful. Teradata is a technology that works very hard and quickly. Teradata is known for its stability and security. 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. These features make it suitable for mission-critical operations. In all kinds of processes, Teradata is very fast.

Other options would be difficult to choose because Teradata has different kinds of scenarios. I considered several alternatives in data management and analytics platforms before Teradata. After comparing architecture, performance, and scalability, Teradata offered the strongest fit for large-scale analytical workloads.

My advice for anyone considering Teradata is to fully explore its scalability and analytical capabilities. Teradata is a strong choice for organizations that need high-performance data processing and want a platform that can grow with their workload. Take advantage of its documentation, best practices, and ecosystem of tools to get the most value from the platform.

Teradata is a strong option for teams looking for reliability, high performance, and an enterprise-grade environment for advanced analytics. I would rate this review a 10 out of 10.

Hybrid Cloud

Other

My main use case for Teradata involves data analytics, data mapping, and data process improvement.

I use Teradata for our in-in processes such as revenue reporting. We have utilized it for procurement to pay and analyzed all the data points from procure to settlements to payments, ensuring that the right data has been sent, checking the data status, and guaranteeing the cleanliness and quality of the data.

We are using Teradata for generative AI related to data mapping, data content, and data distribution.

The best features that Teradata offers include the security around the data, which has been a key issue for us. Teradata locks down the data, providing us with a quality of data we can trust when sending it to external portals and third-party tools such as Salesforce.

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. Additionally, we rely on it due to our HIPAA requirements.

The analytics features and the dashboards in Teradata have been extremely helpful for us.

Teradata has positively impacted our organization by allowing our team to reduce from 27 people down to eight, consolidating our headcount. It has resulted in better performance improvement within our team as we now cover nine business units instead of 18, thanks to the data performance, which has increased data visibility and helped the enterprise achieve a higher rate of internal return on financials.

We have observed a lesser level of churn with our customers; we have been able to maintain more customer relationships because we have access to more data and analytics, allowing us to predict customer needs anywhere from three to nine months ahead of time.

The limitation we encountered was related to speed, prompting us to increase our AWS cloud thresholds and benchmarks on the servers, adding more throughput. We have been testing that, but speed remains one of our key issues.

The integration has been awesome because of the platforms and their usage, and usability is great. The most challenging aspect is finding Teradata resources, so we are focusing on internal training and looking for more Teradata experts. If Teradata could provide a list of certified experts, that would be fantastic.

I rated Teradata a nine because the main area for improvement is the bandwidth of resources that could assist us in applying to our projects, specifically more certified and expert resources. The market share of Teradata professionals is limited, creating an issue for us.

I have been using Teradata for 12 years.

Teradata is stable.

Teradata's scalability is great; it's been awesome.

The customer support for Teradata has been great.

I would rate the customer support a nine on a scale of 1 to 10.

Positive

I previously used Oracle Data Store, but we switched due to problems with integration, speed to market, talent, and security issues.

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%. Our target is a 30% internal rate of return. Overall, our performance improvement and KPIs have increased by about 22%, but we are aiming for another 17% to capture in synergy dollars.

Our experience with pricing included a staged pricing model based on units and servers, the cost per subscription, and the data usage, which involved about five different points in our pricing model. We spent roughly $295,000 on setup costs, and we have a five-year license.

Before choosing Teradata, we evaluated several options, including Snowflake, Teradata, Oracle Data Store, SAP MDM, and two other products whose names I don't remember.

My advice for others looking into using Teradata is to conduct co-pilot type projects first; I recommend executing use case projects to truly see the value and understand how much it helps.

We are just a customer and do not have any other business relationship with this vendor.

On a scale of one to ten, I rate Teradata a nine.

Public Cloud

Amazon Web Services (AWS)

There are two major use cases for Teradata. One is that whatever data we cleanse and aggregate, we push it to Teradata for our business users. We create some ETL pipelines and automate them. The second use case is for data wrangling. Whatever data we publish to Teradata is used for various analyses, various SQLs, and a lot of dashboards sit on top of Teradata.

My most recent project was for inferring the Net Promoter Score for one of the largest Australian banks where I used Teradata for ETL and data analysis. The entire cleaned data of the bank was stored in Teradata, wherein we had eight to ten different datasets coming in from different sources that were aggregated or converged into Teradata. Using that data, we developed certain business rules on top of that aggregated dataset, which was further fed into Tableau that sat on top of Teradata. Using that data, we were able to infer the customer Net Promoter Score for a rolling six-week average.

The first thing that I appreciate about Teradata is its multi-parallel processing. Whatever queries we execute on Teradata, they are blazingly fast, so it offers really fast connectivity. Secondly, it also provides the MultiLoad feature, by which I can upload my Excels directly to Teradata or CSVs to analyze the data. The third feature is the QUALIFY or ROW_NUMBER keywords that I really appreciate about Teradata. The fourth thing is the way Teradata stores data in a columnar format for faster query processing, which is also one of the best features.

The multi-parallel processing and fast query execution of Teradata have benefited me and my team greatly. What really happens is that we store multiple copies of the data, one in Teradata and the other in our HDFS or object storage. When we had to query the data from the object storage, it was really slow, but when we discovered that this dataset is also available in Teradata, it was really fast, especially related to the NPS project that I was discussing. That is probably one of the use cases that I can recall.

Teradata has positively impacted my organization since its inception. Earlier, whatever data we used to house was in HDFS, then we migrated to cloud, and now we are using Teradata, but Teradata has also moved to cloud. Teradata has immensely helped our organization to fetch the data at a faster rate, which has saved us quite a lot of time. That is probably the very best thing about Teradata.

Teradata could be improved by having a web interface that can really help users to plug and play. Right now, what is required is that I have to install a desktop app for Teradata and then set up the connections. If the same thing were available in a web interface, that would be really helpful.

Since I started my career, I have been using Teradata. It has been more than seven years that I have been using Teradata.

Teradata is stable.

The scalability of Teradata is really great. Whenever we need more resources, we can add that in Teradata, and when not needed, we can scale it down as well. All in all, it is very good.

I have not used other solutions personally, but I have seen a use case of Redshift getting used earlier in my current organization. I have also seen the use of object storage prior to using Teradata fully. What I can see now is that we are moving away from object storage because we want faster results, which is why we use Teradata.

I have seen a return on investment through time saved, specifically saving fifteen to twenty percent of the time.

At least fifteen to twenty percent of our time has been saved using Teradata, which has positively affected team productivity and business outcomes.

Before choosing Teradata, I evaluated other options such as Snowflake and Redshift. These were some of the options available.

My advice to others looking into using Teradata is to go for it if you need faster processing, multi-parallel processing, or more security. I would rate this product an eight out of ten.

Hybrid Cloud

Amazon Web Services (AWS)

My main use case for Teradata is mostly for data warehousing and analytics. Teradata is used to store customer data; specifically, accounting transactions from multiple sources. The data is kept for analytical purposes for users. Since the data is very large-scale, an MPP architecture RDBMS was needed to store it. Hence, Teradata was chosen.

Teradata's MPP feature allowed the team to handle huge data sets on the scale of petabytes and perform analytics on those in near real-time when users queried for that data. Teradata has positively impacted the organization because it was selected after extensive market research to identify the best tool for data warehousing. Teradata helped to scale better as the initial minimal data set started increasing rapidly. The scaling capabilities of Teradata really solved that problem.

There is nothing much on the improvement side that I wish was different or better for Teradata. Nothing comes to mind regarding improvements. I give it a nine because I still believe there is always room for improvement, especially when it comes to enhanced availability and possibly better performance and agility. There are no improvements needed for Teradata that have not been mentioned yet.

I have been working in my current field for almost 15 years.

Teradata is stable.

Teradata was very scalable, and that was one reason why it was chosen. Compared to a cloud-native tool, Teradata's scalability was good, and it did scale well.

Teradata's customer support was excellent.

Positive

Before Teradata, Redshift was used initially, mostly due to cost constraints and flexibility. The move was made to be mostly cloud-agnostic.

After implementing Teradata, there was not noticeable latency. I would not say it was very performance efficient, but it was definitely good. Performance was really good, but I could not compare it with anything else because I have not extensively worked on any other data warehousing system. Teradata is mostly what I worked on, so it was really good.

I was not directly involved in pricing, but there was a team handling that. Overall, I think it was good or on par with expectations, otherwise the finance team would not have approved it.

I have definitely seen a return on investment because with any other tool, more manpower would have been needed to maintain it.

Redshift was evaluated before choosing Teradata.

My advice for others looking into using Teradata is to do homework; there is no silver bullet for your problem. You have to find out what works well for your specific needs. If the priority is to find the best tool that can be relied on with less maintenance and good performance, Teradata is definitely a good option, but again, homework must be done to see which one suits the data workloads. I give this product a rating of 9 out of 10.

Public Cloud

Amazon Web Services (AWS)

I have been using Teradata for more than one or two years.

My main use case for Teradata is creating tables, views, and procedures for our applications, along with creating BTEQ scripts to schedule some pipelines.

A specific example of how I use Teradata in one of my projects is for a large retail chain that wants to understand the customer's buying pattern across different regions to improve their store sales and inventory planning, where we have used Teradata for data integration, collecting data from multiple source systems such as POS systems, online transactions, and CRM, handling billions of transactions efficiently with advanced analytical queries.

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.

Parallelism helps my team mainly for scripting and automation in projects; for instance, we utilize Teradata's parallel processing engine to execute queries across multiple model processors simultaneously, making data migration more efficient by splitting large datasets into smaller files and running multiple BTEQ scripts in a parallel fashion.

One challenge I have faced regarding the main use case is the integration with AI, as Teradata does not have the AI models that other OLAP systems such as BigQuery provide, making it difficult for us to give proper recommendations without using different tools for AI integration.

Teradata can be improved on the cloud side by integrating with some OLAP features and providing capabilities similar to Delta Lake for storing semi-structured, structured, and unstructured data.

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.

I have been working in my current field for almost more than four years.

Teradata is stable in my experience.

Teradata's scalability is good.

Teradata's customer support is good.

Neutral

I have not used any other solutions before Teradata.

I purchased Teradata through the AWS marketplace and believe it would benefit from easier documentation for deployment, as it is somewhat complicated for new users.

My experience with pricing, setup cost, and licensing of Teradata is almost good, although the cost is slightly high, but they provide good features.

Before choosing Teradata, we evaluated other options such as Databricks, which we preferred due to their strong AI ML features and options such as Delta Lake.

My advice for others considering using Teradata is that if their use case involves daily transactions with commands such as insert, update, or delete, they should go ahead and use Teradata, as many companies in retail, CPG, and banking find it effective for those operations compared to other platforms. I rate this product 7.5 out of 10.

Private Cloud

Amazon Web Services (AWS)

My main use case for Teradata is primarily storing data and managing the client base, and it was used for batch processing and parallel batch processing.When I say storing data and managing the client base, a specific example of how I use Teradata for that is handling the daily transactions of various customers in the telecom domain, where client transactions are stored in various clusters, so we use Teradata for extracting and loading.Teradata is used for extracting and loading the data as my primary application.

The best features Teradata offers, in my experience, include fast performance during multi-parallel processing, very good scalability, support for linear scalability, and its suitability for handling large volumes of data; additionally, 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.These features help me in my daily work environment as I have used Teradata as a source and a sink to store and analyze data for different reports; however, I did not use it for my personal day-to-day work since it is not required in my day-to-day environment.Additional features which I have heard about but have not used include parallel architecture, which is robust and makes Teradata highly effective for large-scale and analytical reporting.Teradata positively impacts my organization and projects by being extensively used whenever data is required or while batch processing various transformations and aggregations needed for business insights, resulting in complex queries running very efficiently as large volumes of data are easily extracted, transformed, and loaded into various clusters, contributing to a good architecture.Specific outcomes I have noticed include that Teradata helps reduce the time duration for various complex queries involving window functions and various aggregators such as sum and average; when processing a large set of volume data from various clusters, that sort of retrieval is very efficient and good for loading.

Certain challenges I have faced while using Teradata involve the initial setup, which can sometimes get difficult and requires specific rules; additionally, I have found less documentation available, necessitating help from open-source community pages and third parties, thereby presenting complexities in certain tasks.The interface of Teradata can be improved, as various competitors involve cloud-based solutions that feature more drag-and-drop methods for loading and trial methods along with configurations; the user interface could be enhanced in various settings instead of relying on terminal-level operations.

I have been using Teradata for approximately two to three years.

Teradata is stable; however, I have encountered certain errors that required debugging from time to time.

The scalability of Teradata is flawless; however, there are many configurations needed.

Regarding customer support for Teradata, I have not directly interacted with it, but I do know that we generally get quick responses; most of our cases get resolved through community pages rather than direct customer support.

Before Teradata, we also used Snowflake and Databricks, which are primarily cloud-based solutions; we incorporated Teradata for our on-premises needs.

Certain challenges I have faced while using Teradata involve the initial setup, which can sometimes get difficult and requires specific rules.

My experience with pricing, setup cost, and licensing indicates that while it was not part of my work, I found Teradata to be somewhat costlier when considering other options.

I evaluated other options before choosing Teradata, specifically Snowflake and Databricks.

Teradata handles a large volume of data effectively, being beneficial in a specific domain where a large amount of data is flowing, providing a reliable system that performs multi-parallel execution and batch processing for loading.I would suggest to others considering Teradata that if they are looking for something specific domain-wise and infrastructure that can handle multi-parallel and large volumes of data for extraction and loading, it would be a great tool. I would rate Teradata an eight out of ten.

Hybrid Cloud

Google

Teradata's main use case for me is writing SQL according to business requirements from customers.

I can give you a specific example of how I use Teradata for a business requirement: recently, a customer had a requirement to look into the population of patient data in the healthcare industry, such as how many patients were discharged before a specific time of the day, and they had a couple of other conditions as well. I wrote the code to exclude those conditions and get the data of whether the patient was discharged before that given time or not.

I use Teradata mainly for SQL logics. When a customer has a business requirement, I write the SQL code in Teradata to meet those customer logics. I also do some side projects such as creating views to help me monitor or get to know the data a bit better.

Teradata offers great features; it is fast, faster compared to the previous applications that I used, and the scalability is good. The ability to run jobs in parallel is a good use case from Teradata.

The ability to run jobs in parallel has made my work easier. With the processing of jobs sequentially, that would hold the jobs and take more time compared to parallel processing inside Teradata. For instance, if we have data with 5 million records and we process it sequentially, then it has to go from one to 5 million. In Teradata, we can divide it into five different AMPS and process it as one to 1 million, five times in parallel. This is done based on the primary key; the data will be divided and processed in parallel to get the work done in 20% of the time compared to the previous one.

I can say that Teradata has positively impacted my organization, as the scalability and the performance has helped my organization and my team especially to process data better.

I can share a specific example of how scalability or performance improvements have helped my team: previously we were using Netezza, and compared to that, the data is getting processed in Teradata faster, and the data is ever-growing. Once we were struggling to process a particular amount of data previously, but in Teradata, due to the scalability, we were able to process it without any issues.

I choose 8 out of 10 for Teradata because there is always the scope for improvement. Currently, there are other no-code, low-code tools in the market.

I have been using Teradata for close to two years now.

In my experience, Teradata is stable; I have not seen any downtimes or reliability issues.

Teradata is helping us scale the data at the pace that we are going, so it is helping in scalability.

I have not contacted Teradata support because I have got all the support from the documentation that is available online.

I previously used Netezza, and for performance and scalability, we chose Teradata.

I am satisfied with my thoughts about Teradata. I would rate this solution an 8 out of 10.

It’s good. The educational resources are good. I think the idea of distributed computing is well implemented in Teradata, and that was likely their intention from the beginning. It's a foundation for big data processing. So far, I appreciate the product, but I haven't worked on a real project with it yet.

The courses are good. I don’t have a full certification yet; I just have some course certificates. In my first week, I completed around five or six courses, and now I work on a longer one. The content is well-organized, and I’m happy with the learning materials so far.

The data processing, clustering, and distributed computing are impressive. I’m curious to see how it works internally and how performance is accelerated. I’m also learning about how SQL and Teradata’s EXPLAIN feature work. So far, it's a very good product.

Teradata do have some AI models that can be used for in-database analytics. I haven’t tried them yet, but I know the product's K-Means implemented in the database, which is interesting because I’ve seen how challenging it is to parallelize K-Means in other environments. I plan to explore it more when the opportunity arises.

K-Means is implemented, and Teradata leverages its database operations for AI analytics. They use parallel processing, which is one of Teradata's main features.

I find Teradata's approach useful in its current state. I definitely want to explore it further.

Teradata has a few AI models, but in data science, we need more flexibility. We can’t be limited to what's pre-built in the database. Typically, data science projects require experimenting with different models, so the limitation is that Teradata only has basic machine learning models in its database. Data science requires more advanced modeling, and you always want to search for the best possible approach. Combining the capabilities of Teradata with custom data science models will take time to mature, but it shows promise.

Teradata needs to promote it more. If they're the first to introduce things like in-database AI, they should really focus on promoting that. I haven't heard much about it, but maybe that's because the environment I’ve been working in recently has been mostly open-source. I’ve been doing applied research and freelance work that didn’t rely on robust vendor products, so I never got a chance to compare Teradata to others. I have heard about Databricks, though.

I started using it this month, so my experience is very recent.

I've worked a lot with open-source tools, mainly Python, in my role.

I’ve worked with IBM Cognos before, but that was just part of a solution, mainly for VPN dashboards. However, I wasn’t a specialist in business intelligence, so this is new to me. Teradata complements my data science journey.

From my perspective, I only started using it because it's needed for my current job. Before this, I didn't consider Teradata better than Oracle or GV2A. I think it's better than GV2A, but Oracle is more robust. Teradata has its customers, but I didn’t really compare them before because business intelligence and data warehousing were not my areas of focus. IBM was behind both Oracle and Teradata in this field, but I am not sure exactly how Teradata stands in comparison. 

In my data science journey, I realized that my weak point was data analysis and data warehousing, which is why I’m happy to be working with Teradata now. It's helping me fill that gap.

I’ll be recommending it to customers. In my country, it is very active in acquiring data analysis solutions, so it will likely be recommended for that sector.

I have very limited knowledge at this point. I'm still exploring the architecture. From what I’ve learned so far, I believe it's used quite extensively in my region. The idea of distributed computing and partitioning is definitely something that's needed.

Also, the cloud and on-premises architectures are not that different, which is a positive aspect.

Overall, I would rate the product an eight out of ten. 

Our primary use case for Teradata is as a data warehouse; we store our databases for the data warehouse, including the EDW. It is mainly used for our data warehouse environment where we run a lot of analytics and heavy queries.

The solution provides good performance for our analytics queries. Due to the capacity of the Teradata warehouse, our queries are well performed.

The data mover is valuable over the last two years as it allows us to achieve data replication to our disaster recovery systems. We use the Teradata mover utility, which is presently valuable.

Replication needs improvement because we currently use DataMover once daily. Unlike SQL and Oracle, which have in-built replication capabilities, we don't have similar functionality with Teradata. 

Additionally, when backups are performed, it locks the database, preventing user access, which is a concern. Also, we need extra servers to perform backups, unlike SQL, which has embedded backup capabilities.

I have been working with Teradata for the last four years.

I find the stability to be almost a ten out of ten. It is very stable in our experience.

Scalability is complex as you need to purchase a license and coordinate with Teradata for additional disk space and CPU. They enable a small percentage for scaling, which isn't straightforward.

Customer support is very good, rated eight out of ten under our essential agreement, although it depends on the license agreements.

Positive

We also use SQL and Oracle. Teradata is much more expensive, however, it was already in place at our company.

The setup process is complex. Teradata installed and configured the systems.

We used Teradata's services for installation and configuration.

The pricing is quite high. It's a two out of ten where one is very high priced. Teradata is much more expensive than SQL, which is well-performed and cheaper.

I prefer SQL for its cost-effectiveness and performance and also consider Oracle, although its licensing is more expensive. Teradata was already present when I joined the company.

For Teradata to perform well, it requires good configuration and a good structure of the database. It's most suitable for medium and above-sized companies rather than small ones.

I'd rate the solution seven out of ten.

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 excellent because of Teradata's parallelism. It doesn't impact operations when nodes are added. This allows customers to expand without migrating the entire database or system.

The most valuable aspects of Teradata are not specific features. Rather, it's the overall performance, particularly parallelism, workload management, and parallel computing. Teradata effectively uses parallelism to the granular level, performing better than other databases.

Teradata is somewhat late in adopting cloud technology. They need to focus on the adoption of cloud to remain competitive and target customers who prefer not to invest in capital expenditures and seek a more flexible, operational expenditure approach.

I have worked with Teradata for around 15 years, from Teradata version six to version 14.

Teradata is highly stable. The workload management and software maturity provide a reliable system, unlike some newer cloud software that can exhibit misbehavior.

Even if an organization starts small, Teradata offers the flexibility to expand by adding nodes or more storage, especially in cloud environments, without incurring downtime or taking systems offline.

The technical support from Teradata is quite advanced. However, like any support service, there can be delays. I rate the support as eight out of ten because of their technical expertise.

Positive

During my career, I've been involved primarily with Teradata, and some clients are migrating from Teradata to other technologies.

The initial setup process is rated as eight out of ten. It's straightforward. That said, when migrating databases from other systems, challenges arise in redesigning the code to optimize parallelism.

The implementation is managed by a separate customer service group within Teradata, not by me or my group.

Teradata is high-quality at a premium price. Initially, it may seem expensive compared to similar cloud databases, however, it offers significant value in performance, stability, and overall output once in use.

New users should focus on understanding Teradata's parallelism and write their queries or codes to utilize its full potential. Knowing how to use workload management effectively is beneficial.

I'd rate the solution eight out of ten.

Public Cloud

Other