I use Teradata Vantage for various tasks like figuring out customer churn rate and running Python scripts against my large datasets. The biggest headache it saves me from is the long waiting time for getting the required data. I no longer need to export data to CSV; I can directly query Teradata Vantage and get results promptly. I like the SQL engine's power of Teradata Vantage the most because joining queries on millions of rows just works like a charm. Instead of searching through the whole table, it goes straight to the record. Teradata Vantage's primary index strategy is what makes it faster than other tools out there. Setting it up with Azure was easy, especially after following the documentation correctly.

What I like best about Teradata Vantage is its ability to handle large scale data processing with strong performance and reliability. It works well for complex queries and analytics workloads, especially when dealing with huge datasets. I also like the flexibility it provides with support for multiple analytics approaches like SQL, machine learning, and advanced analytics in one platform. This makes it useful for both technical and business teams.

So i did some poc using agent builder it built some poc around parallelization design of agent flow which really helpful and used its vector store for the rag pipeline i Integrated 1 agent with the rag pipeline tge integration within the tools are seemless.