Teradata Reviews

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.

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.

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.

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.

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

The tool's most valuable feature is the warehousing model. 

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.

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.

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.