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.

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.

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. 

I use the solution in my company for reporting.

The tool's flexibility and capacity for expansion are areas of concern where improvements are required.

I have been using Teradata for two years. My company is an end user of the product.

It is a stable solution. Stability-wise, I rate the solution a nine out of ten.

In our company, when it comes to Teradata, we have an appliance in place, and we plan to upgrade to another appliance that offers more capacity.

I rate the product's scalability to be below average.

Around 100 people in my company use the tool.

The tool is used daily in my company.

It is difficult to get someone with technical expertise involved with the solution's technical support team. I rate the technical support a four out of ten.

Neutral

I only have experience with Teradata.

I rate the product's initial setup phase as above average on a scale of one to ten, where one is difficult, and ten is easy.

The solution is deployed on an on-premises version.

For installation, my company only used the services offered by Teradata.

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

My company has evaluated other options against Teradata, and I see that it has been used in our company for the past four years.

The tool did enhance our company's data warehousing and analytics capabilities.

Teradata was crucial for our company's data analysis, especially for every campaign we run, including the pricing, exercises, and analysis.

Teradata's feature, which had the most significant impact on our company's data management, revolves around the analytics feature it offers.

I rate the tool an eight out of ten.

The use cases vary based on the projects. In most projects, I worked with ETL tools like Informatica alongside Teradata. However, there was a specific project where we built a real-time data warehouse for active directory data using Teradata.

Oracle was the source system, with potential additional sources feeding into the Oracle database. We used Unix scripting to extract data from Oracle and leverage a colleague's expertise in Unix for the Teradata portion. 

We wrote ETL queries and performed data profiling before loading the data into the target data warehouse for further processing by other tools.

Our task was to create a data warehouse, an active data warehouse in Teradata.

We created and constructed the warehouse. We used multiple loading processes like MultiLoad, FastLoad, and Teradata Pump. But those are loading processes, and Teradata is a powerful tool because if we consider older technologies, its architecture with nodes, virtual processes, and nodes is a unique concept. 

Later, other technologies like Informatica also adopted the concept of nodes from Informatica PowerCenter version 7.x. Previously, it was a client-server architecture, but later, it changed to the nodes concept. Like, we can have the database available 24/7, 365 days. If one node fails, other nodes can take care of it. Informatica adopted all those concepts when it changed its architecture. Even Oracle databases have since adapted their architecture to them. 

However, this particular Teradata company initially started with its own different type of architecture, which major companies later adopted.

It has grown now, but initially, whatever query we sent it would be mapped into a particular component. After that, it goes to the virtual processor and down to the disk, where the actual physical data is loaded. 

So, in between, there's a map, which acts like a data dictionary. It also holds information about each piece of data, where it's loaded, and on which particular virtual processor or node the data resides. Because Teradata comes with a four-node architecture, or however many nodes we choose, the cost is determined by that initially. So, what type of data does each and every node hold? It's a shared-no architecture. 

So, whatever task is given to a virtual processor it will be processed. If there's a failure, then it will be taken care of by another virtual processor.

Moreover, this solution has impacted the query time and data performance. 

In Teradata, there's a lot of joining, partitioning, and indexing of records. There are primary and secondary indexes, hash indexing, and other indexing processes.

To improve query performance, we first analyze the query and tune it. If a join needs a secondary index, which plays a major role in filtering records, we might reconstruct that particular table with the secondary index.

This tuning involves partitioning and indexing. We use these tools and technologies to fine-tune performance.

When it comes to integration, tools like Informatica seamlessly connect with Teradata. We ensure the Teradata database is configured correctly in Informatica, including the proper hostname and properties for the load process. We didn't find any major complexity or issues with integration. But, these technologies are quite old now. 

With newer big data technologies, we've worked with a four-layer architecture, pulling data from Hadoop Lake to Teradata. We configure Teradata with the appropriate hostname and credentials, and use BTEQ queries to load data. 

Previously, we converted the data warehouse to a CLD model as per Teradata's standardized procedures, moving from an ETL to an EMT process. This allowed us to perform gap analysis on missing entities based on the model and retrieve them from the source system again. We found Teradata integration straightforward and compatible with other tools.

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.

I have been using this solution for seven years. 

Teradata's technical support is responsive. Although I haven't directly interacted with them, my colleagues at Captivine have. 

They've efficiently resolved queries, adhering to SLAs, often within two to three days, even with time zone differences between India and the US. This quick turnaround is especially crucial for client or production issues that require urgent attention.

We didn't frequently encounter issues with Teradata because the tools are robust and well-documented. Teradata provides comprehensive documentation and resources with the purchase of a license, aiding our day-to-day operations.

There was one exception case, but overall, we managed well with the resources provided by Teradata.

Neutral

Currently, I work closely with the AI and ML team, focusing on frameworks like NumPy.  

At my company, we use Snowflake as a data warehouse. I primarily worked on the ETL side, ensuring data accuracy, governance, modeling, and loading data into the Snowflake warehouse. But it was part of a particular project. 

I've also used Oracle as a source and target, as well as SQL Server. In my experience at Capgemini North America, most clients like Elkhared, Levi's, General Electric, etc., used Teradata as their primary database. 

While the architecture has evolved with new technologies, Teradata remains a powerful tool.

Teradata is an expensive tool.

Teradata, along with Informatica, is expensive. Teradata is still widely used in the Middle East, especially in banks across Dubai and Saudi Arabia, indicating its continued relevance. Compared to open-source solutions and other market offerings, Teradata remains on the higher end of the pricing spectrum.

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.

Considering its strength in data warehouse technology, I'd rate it between a nine out of ten. For data warehousing specifically, it has a lot of built-in components that can handle various requirements.

However, there's also big data technology to consider these days. But for a traditional data warehouse environment, Teradata is a very successful solution, a very good database, actually.

We used its capabilities for critical tasks, particularly in the realm of recovery jobs. We capitalized on the database's ability to transfer entire blocks of data rather than just transactional information. This approach, especially when dealing with block-level data, proved significantly faster compared to other techniques such as UBC WAN.

It is a highly robust software solution.

The primary challenge with Teradata lies in its cost structure, encompassing subscription fees, software licenses, and hardware expenses. The management of these pricing components can be notably high. I believe there's room for improvement and investment in Teradata's ETL engine, making it more competitive with tools like IBM DataStage. Considering the growing importance of big data ecosystems, it could benefit from enhanced compatibility with platforms like Cloudera and tools like Apache Spark. It's essential to bridge the gaps and make Teradata's tools more accessible and user-friendly in the evolving landscape of data virtualization and analytics.

I have been using it for three years.

I would rate its stability capabilities nine out of ten.

I would rate its scalability abilities eight out of ten.

The tech support has been commendable. They adhere to the SLAs and respond promptly to our issues. We haven't experienced any significant delays on their part. I would rate it nine out of ten.

Positive

I've collaborated with a multitude of vendors, including Dell for EMC storage, Oracle, Nautilus, and Ericsson for the solution team and platform methodologies. I'm currently deeply involved in projects centered around Big Data and the emerging Cloudera ecosystem. The primary factor driving the adoption of Cloudera and similar big data environments is the cost efficiency they offer, coupled with their ability to seamlessly integrate with multiple data sources.

The initial setup was straightforward.

The deployment process doesn't require an extended timeframe, it can be accomplished in just three days. Additionally, you may allocate an extra two days for preliminary preparations, such as gathering data from the current data house. Approximately 20% of the data house considerations have been factored into the planning. I've encountered certain challenges during the database server upgrades, notably transitioning from version 15.7 to 16.2. Despite being a recurrent process, the notable differences were primarily observed in the extraction engine, particularly in the increased number of workflows. Interestingly, these disparities were more evident in the extraction processes rather than within the database itself.

The cost is substantial, totaling around $1.2 million, solely dedicated to upgrading the hardware. This amount doesn't even cover the software upgrade for the database itself, which comes at an additional cost ranging from two to three thousand dollars. Specifically, for the hardware upgrade, we're dealing with a significant investment, around $1.5 million, involving approximately sixty servers with a combined storage capacity of around twenty-two terabytes. These expenses constitute a substantial financial commitment, especially when factoring in standard costs.

I actively endorse the use of Teradata, particularly for data warehouse solutions, due to its robust handling of raw data and overall stability. The emphasis on retaining raw data is its key strength. However, it's crucial to acknowledge the cost associated with it. To address this, consider forming teams to strategize and mitigate expenses, ensuring a more cost-effective implementation that aligns with your organization's objectives. Overall, I would rate it nine out of ten.

We use Teradata for our organization's data warehouse, where we store data from all enterprise sources. With Teradata's efficient processing, we generate business intelligence reports and support analytics initiatives. Our teams connect to Teradata for valuable insights, enabling data-driven decision-making across departments. The platform's analytics capabilities enhance our understanding of enterprise data for strategic planning and optimization.

The most valuable feature is parallel processing architecture. Teradata's capabilities enhance data management efficiency, support scalability, and contribute to faster query performance, enabling us to derive valuable insights and analytics.

In our experience with Teradata, we haven't seen any significant weaknesses. However, when it comes to scalability, limited interest and success in some areas make us hesitate about upgrading. In the future, I would like to see Teradata incorporate features that allow it to handle unstructured data. Additionally, improvements in documentation to make it more user-friendly and up-to-date would enhance the overall user experience.

I have been working with Teradata for almost six years.

It is quite stable.

It is fairly scalable. In our company, we currently have around 30 to 40 developers working on advanced data development using Teradata.

I would rate the support as an eight out of ten. They are quick to respond to our questions and have been helpful not just with problem-solving but also during patch deployments on the provider.

Positive

Before Teradata, I primarily worked with databases like Oracle and Db2, mainly in banking. Teradata's advantage lies in its parallel processing, which sets it apart from competitors like Oracle and Db2. The efficiency in handling large-scale data and analytics makes it a preferred choice in my experience.

My advice for those considering Teradata is that if you are looking for an optimized database, it is worth considering. In my experience, it has been better than Oracle and DB2 in handling large-scale data and analytics efficiently. Overall, I would rate Teradata as an eight out of ten.

We use it as a staging database in our organization, serving as a repository for financial models.

The current operational approach needs improvement. In the past, relying on infrastructure data staging provided flexibility in collaborating with any database provider. However, transitioning to Informatica and integrating it with various data services proved beneficial for serving multiple clients. Despite its advantages, this approach incurred substantial costs in terms of hardware and technical expertise. To address this, there is a need to develop and offer free content and workshops, providing guidelines and technical knowledge for efficient data operation.

I have been working with it for seven years.

I would rate its stability capabilities eight out of ten.

I would rate its scalability abilities six out of ten. While it provides scalability, it is notably complicated.

We previously used IBM, but due to the market requirements, we switched to Teradata.

The initial setup was straightforward.

It takes a couple of hours to be completely deployed.

It comes at a notably high cost for what it offers. Apart from the standard licensing fees, there are supplementary costs associated with the licensing course. I would rate it one out of ten.

Overall, I would rate it eight out of ten.

Teradata is a great, industry-leading data warehousing product that has MPP architecture. In recent times, there have been many other products available in the cloud space. It has a lot of other features like linear scalability and unconditional parallelism.

Teradata hardly supports unstructured data or semi-structured data. Teradata is very costly, and it's not easy to upgrade or expand the components in Teradata. A lot of infrastructure needs to be set up in order to use Teradata.

I have been using Teradata for more than 15 years.

Teradata is a very expensive solution.

Many other products equivalent to Teradata are available in the market, like BigQuery, Redshift, and Snowflake.

Teradata is a suitable solution for small customers.

Overall, I rate Teradata an eight out of ten.

We use Teradata as our company's data warehouse engine to process tons of data since we are the Central Bank of Columbia, where we use that for statistics to study several areas of banking. Our main database, data warehousing, data mining, and back office statistics are done with the help of Teradata. We heavily use Oracle as our transactional database, but we use Teradata for all other purposes.

I am not exactly the person doing the programming and exploring the product's different features. To get information on the product's features, I will have to contact some engineers in charge of programming. The most valuable feature of the product is the ability of the product to process huge amounts of data without creating conflicts in the queries. The way the database and IntelliFlex are designed, you can process several queries from different applications simultaneously, and they don't conflict with each other. I mean, I think that's the reason why we find improvements in our query time. The way the queries are internally in IntelliFlex process makes them less complicated. In other databases, when you start a query, the dataset is put on hold for a while, so all the queries have to wait for the first query to finish. IntelliFlex and Teradata are designed to avoid such an issue because I am unsure. I feel Teradata IntelliFlex can function in the aforementioned manner since they manage smaller sets of data, because of which there are no conflict queries between one and the other.

I don't have the exact details of the improvement needed in the performance of Teradata IntelliFlex because I work with the infrastructure department. The infrastructure department is in charge of getting infrastructure for the system to work, but before having Teradata, the data processing was taking a lot longer than it is now. We got into Teradata because we needed fast processing of huge amounts of data since, in the past, we were trying to achieve it with Oracle databases, and it wasn't good enough. In the last two years, the processing times have reduced by a third or a fourth of the time without causing any problems because of the efficiency of the Teradata engine.

The only issue our company has with Teradata IntelliFlex is that it is not cost-effective because of the way the product has been designed. We have Teradata IntelliFlex on-premises because we pay for a lot of muscle that we are not using. The problem we're seeing is that when we try to move into Teradata IntelliFlex's cloud services, we notice that Teradata doesn't have a flexible price structure to allow you to use a fraction of IntelliFlex's extension and charge just for the use of the solution. We receive messages from Teradata asking us to pay for the services we use on-premises, while in reality we use less than 30 percent of the machine's capacity.

There is something that is not working in favor of the business models for IntelliFlex when you plan to move to the cloud.

I have been using Teradata IntelliFlex for two years. Our company plans to renew the solution's latest version, but the hardware is around two years old.

Stability-wise, I rate the solution a nine out of ten. In the two years we have been using the platform, we have had only one or two issues that got the service out for a few hours.

The product's scalability is great. Scalability-wise, I rate the solution a ten out of ten.

I rate the technical support a nine to ten out of ten.

Positive

The initial setup was straightforward. Before IntelliFlex, we were using Teradata's 2700 and 2800 appliances. We were moving from a Teradata appliance to a new Teradata appliance, and during that process, we were able to move all the data in between three to six months without a problem, keeping both platforms working until we moved into IntelliFlex. The setup phase was a good process.

The solution is deployed on-premises. We are planning to move to the cloud in the future.

We feel that we are given the services from Teradata IntelliFlex for what we are paying. We are the Central Bank of Colombia, a non-profit organization, making it difficult for us to say that because of IntelliFlex, our earnings have increased. My institution doesn't aim for any earnings. We have been able to provide much better services to all of our customers, including all the financial institutions in our country, because of which I can say there has been a return on investment using Teradata IntelliFlex. A return on investment is possible based on the improvements in our service to our customers.

Our company is just looking to see if there is another product other than Teradata IntelliFlex that we can use in the future, not because we are not happy with the product. The reviews on Teradata IntelliFlex point out that it is a very solid and efficient solution, but we are slightly worried about the cost.

The price and the pricing structure should be improved.

Two years ago, we had plans to move into the cloud since we were doing so with all the products we have in our company to avoid having our own data centers. When we tried to move Teradata IntelliFlex to the cloud this year, we noticed that at this point in time, we might have to stay on-premises for a couple of years or until Teradata provides us with a better price structure. Owing to the aforementioned reasons, we have been considering moving to another more flexible platform and more price convenient for our company.

We are happy with the services provided by Teradata IntelliFlex.

At this point, it's worth looking at other alternatives other than Teradata IntelliFlex, depending on the kind of use you plan to do with the platform. There are certain areas of our company where we have noticed that we could get the same benefits from another platform, especially from the ones which are not pricey. If you really want to take advantage of the wonderful performance and speed that Teradata provides, then you need to choose it. But if you don't need the level of performance offered by Teradata, you can look for other alternatives. 

Overall, I rate the tool a nine out of ten.