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.

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.

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.

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.

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.

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.