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.