Teradata VantageCloud Lake on AWS

Teradata 's main use case for me is writing SQL according to business requirements from customers.

I can give you a specific example of how I use Teradata  for a business requirement: recently, a customer had a requirement to look into the population of patient data in the healthcare industry, such as how many patients were discharged before a specific time of the day, and they had a couple of other conditions as well. I wrote the code to exclude those conditions and get the data of whether the patient was discharged before that given time or not.

I use Teradata mainly for SQL logics. When a customer has a business requirement, I write the SQL code in Teradata to meet those customer logics. I also do some side projects such as creating views to help me monitor or get to know the data a bit better.

Teradata offers great features; it is fast, faster compared to the previous applications that I used, and the scalability is good. The ability to run jobs in parallel is a good use case from Teradata.

The ability to run jobs in parallel has made my work easier. With the processing of jobs sequentially, that would hold the jobs and take more time compared to parallel processing inside Teradata. For instance, if we have data with 5 million records and we process it sequentially, then it has to go from one to 5 million. In Teradata, we can divide it into five different AMPS and process it as one to 1 million, five times in parallel. This is done based on the primary key; the data will be divided and processed in parallel to get the work done in 20% of the time compared to the previous one.

I can say that Teradata has positively impacted my organization, as the scalability and the performance has helped my organization and my team especially to process data better.

I can share a specific example of how scalability or performance improvements have helped my team: previously we were using Netezza , and compared to that, the data is getting processed in Teradata faster, and the data is ever-growing. Once we were struggling to process a particular amount of data previously, but in Teradata, due to the scalability, we were able to process it without any issues.

I choose 8 out of 10 for Teradata because there is always the scope for improvement. Currently, there are other no-code, low-code tools in the market.

I have been using Teradata for close to two years now.

In my experience, Teradata is stable; I have not seen any downtimes or reliability issues.

Teradata is helping us scale the data at the pace that we are going, so it is helping in scalability.

I have not contacted Teradata support because I have got all the support from the documentation that is available online.

I previously used Netezza , and for performance and scalability, we chose Teradata.

I am satisfied with my thoughts about Teradata. I would rate this solution an 8 out of 10.

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.