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.

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.

Our use cases were related to banks and other financial institutions. 

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 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.

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.

Teradata Cloud Data Warehouse offers a more cost-effective cloud solution. However, the main challenge we encounter, especially in South Africa, is the limited bandwidth available for connecting to our cloud network. This is where the cost factor becomes significant, making it impractical to fully utilize the cloud due to the bandwidth constraints required for communication with the cloud. This limitation poses a major problem. While we can access certain features of cloud solutions from Teradata for free to evaluate them, it is not feasible for serving a large user base like two to three million people accessing the database. To accommodate such a user load, we would need a massive and robust pipeline connecting to the cloud.

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 use the solution in my company for reporting.

We'd done a lot of replacements of sites. We've installed new systems with Unity support. We installed and deployed around about five systems for a bank in South Africa. When the old Teradata equipment became obsolete, we replaced them with new ones. 

I was involved with the whole thing from start to end. You can't just upgrade Teradata. You have to see if all of the other stuff is compatible with the new site. 

We use Teradata IntelliFlex for data warehousing, which is a way of presenting data for faster retrieval.

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

Our use cases are all about data ingestion, data manipulation, and data exploration. It's the core database for us.

We use Teradata to build data warehouses for our customers.

We use it primarily as a dedicated platform for aggregating structured data, essentially serving as our structured data repository or data warehouse.

I am a Teradata administrator as well as a developer. I have about 700 users who are using our system, and we are maintaining the system in terms of tuning and in terms of all end-to-end items above the Teradata system.

We are using it as a data warehouse. In the data warehouse, we have got so many users who are doing their reporting and different types of analytics work using the solution.

IntelliFlex is used in the banking sector as a production system.

Teradata was mostly a control system for us where we were fetching the data from it on basic time intervals, on various DVs and various schema where we had to fetch the data. Reading was the main operation that we used Teradata for, but while reading, there were complex ENTs that we had to write because of a few data that was supposed to be transformed and then fetched, so that was the major use case for the solution.

We primarily use the solution as a relational database. We use it mainly for logistics. 

Our use case is for data warehousing and BI reporting. We are customers of Teradata.

My primary use of Teradata is for data warehousing.

Teradata Data Lab is used for simulations.

We used the solution for our data warehouse. 

We primarily use the solution for the EDW. 

Teradata is used for many use cases, such as clickstream, website support, and manufacturing control.

We use Teradata in our data warehouse and we extract data. We have a data department that uses many tools for data extractions.

We primarily use the solution for data warehousing. 

Primarily what happens is, in Juniper, they have internal business analytics and intelligence, a business intelligence analytics dashboard, and a lot of databases that capture the customer feedback. 

It actually captures all the internal defects that are tracked on products. There are different databases that accumulate all this data. Basically, you need to be able to source the data and do some filtering. We extract, transform, and load logic. We have to run certain business rules based on what the program or management team looks at. The teams want to filter out some of these problems that they have, and they want to analyze how well the company is responding to many of these customer issues, et cetera.

Our company is a large company with each particular team using its own database and its own database tables. It becomes extremely hard for the company to take the data from different data sources and be able to correlate everything and be able to analyze many of the processes that are happening within. There is a workflow discontinuity that has been observed due to the fact that the data is distributed. In order to understand exactly how a request comes in from the customer and what goes on behind the scenes in order to resolve the problem or to be able to provide the support to the customer, and how long does it take before the problem gets fixed and delivered, et cetera, we need to centralize everything and we use data warehousing for that. 

There are other similar kinds of business rules filtering that happen on the dashboard as well. This enables people to be able to view the data that is needed and the metrics that are of interest to them and is especially useful when it comes to the executive team.

My current use case is an analytical system for an oil and gas company. They have a data lake and they are getting their data from different IoTs and different SAP systems. They wanted to have some feedback about their system and check on their production.

The IoT center's data was actually our use case, then how we can use that data to prevent disaster from happening. Previously, this work was all done manually. If there is a leak in the pipes then it is a large area to search because there are hundreds of kilometers that have been run.

To tackle this problem, they use IoT sensor data. It helps them to understand how the flow is working at a specific point in time, what happens when they're drilling the oil and other types of data. To complete the analysis, we use an analytical system or a Teradata Azure system that we designed.

The ECL was designed in a manner where we were doing the transformation on Hadoop. It is more like a balanced system where you use Hadoop for what it is good for, and where you use Teradata for its purpose.

I have worked in several different environments. The largest Teradata implementation on Azure was done by me. Right now, I'm working with AWS but previously, I have implemented both on-premises and hybrid deployments.

We primarily use the solution for data warehousing.

Aster Teradata is mostly being used for doing mapping and I use it for analytics. Usually, there is a support vendor who will use Teradata to do the database analytics. But sometimes with Teradata, the DB administrators use it to get access to the libraries or the data processes.  

I mostly recommend Teradata for data storage because it is easy to deploy and implement.  

I mainly used Teradata as an EDW and for customizing and integrating CLDM, applying aggregation in the database, and analysis.

Primarily, Teradata is a traditional data warehouse, but we also encourage our customers to adapt to the logical data warehouse and data lab.

Teradata is primarily for data warehousing at large customers, like banks, retailers, and government organizations. However, we don't use Teradata for Russian government organizations because it's a US tool, and the Russian government doesn't allow foreign software.

We use it as a data warehouse.

We are using this solution to check and validated our data.

We resell Teradata, but not in the cloud.

Currently, our experience with Teradata in the cloud is theoretical as we haven't started selling it to our customers yet; however, a few of our customers are interested in migrating to a cloud-based solution.

Our clients use Teradata to consolidate corporate data to provide reports. These reports are required every day — it's more like described analytics.

We use this solution as a simple repository for all of the data that comes from multiple sources related to our organization. It has financial data, transactional data, customer complaint data, as well as customer feedback data. All of these are collected from different systems and they are consolidated for viewing within our management dashboards or for use in other analytics.

Our primary use case for Teradata is reporting. I am a business analyst and I use this solution to first analyze my data and then visualize it. 

We are only consultants, so we don't use the program ourselves. 

We currently use the on-prem model of this solution. Our primary use case of this solution is as an enterprise tech warehouse for business intelligence across different domains. 

We primarily use the solution to store data.

We implement Teradata in financial sectors, which include banks in India and Canada.

Our primary use case for this solution is the Transparency Portal of the Brazilian Government. Teradata provides the database infrastructure for this analytical site that is able to create an online BI experience that provides details on how the Brazilian government spends public money.

We have been using the on-premise PostgreSQL, and most of our data comes from SAP. We have all our Oracle logs being pushed to Teradata, which is where we take the data, then skim it. We pass it through whatever it is we are using, then turn it back to the customers. 

Therefore, our primary use case is to distribute the data across all types of commercial and personal consumers, making it available as fast as possible.

We have all our workloads on AWS.

Our primary use case is for retail and customer behavior modeling. We handle a billion records from different sources to create a consolidated feed for KXEN analysis and customer behavior modeling.

We use it for enterprise data warehouse (EDW) with on-premise hardware. The company uses EDW for most MIS functions.

We tried to use case Teradata for a data warehouse system, but we had some problems in relation to the Teradata system, CDC tools, and source databases. We were unable to transfer data from HPE Integrity mainframe to Teradata.

As a contractor for various companies working in various industries, we usually find Teradata in financial organizations. It is often being used for relational data storage for huge data warehouses (usually built on Inmon principles) or as storage for building analytical data marts. Teradata is positioned as a high-performance solution for heavy analytical queries and, in fact, it is. Moreover, Teradata is typically supplied with the whole ecosystem of its tools (like Viewpoint), but I have not extensively used them.

Business intelligence and data warehousing solutions for our client.

Building a single truth database (Enterprise Data Warehouse) where all the enterprise data is integrated at one place.

Data mart implementation for the insurance industry.

Its primary use is a superfast database.

The purpose that we are using Teradata for is to migrate data to the cloud.

I am using Teradata Vantage Express and I'm using Teradata Studio along with it.