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Kinetica 🤝 Confluent a match made in streaming heaven

By Hari Subhash | February 6, 2024

Every moment, trillions of entities—vehicles, stock prices, drones, weather events, and beyond—are in constant motion. Imagine the vast opportunities and insights we could uncover by monitoring these objects and detecting pivotal events as they unfold, in real time. Such a task demands an analytical engine that can ingest high velocity data streams, execute sophisticated queries…

Kinetica joins the Connect with Confluent Partner Program

By Nima Negahban | February 6, 2024

We are thrilled to announce that Kinetica has now joined the Connect with Confluent Partner program. This collaboration merges the unparalleled speed of Kinetica’s GPU-accelerated database with the data streaming capabilities of Confluent Cloud, delivering insights on high-velocity data streams in mere seconds. Why This Partnership Matters Confluent is at the forefront of streaming data…

How to deploy natural language to SQL on your own data – in just one hour with Kinetica SQL-GPT

By Chad Juliano | January 5, 2024

You’ve seen how Kinetica enables generative AI to create working SQL queries from natural-language questions, using data set up for the demonstration by Kinetica engineers.  What about your data?  How can you make Kinetica respond to real SQL queries about data that belongs to you, that you work with today, using conversational, natural-language questions, right…

The mission to make data conversational

By Chad Juliano | December 13, 2023

I think one of the most important challenges for organizations today is to use the data they already have more effectively, in order to better understand their current situation, risks, and opportunities.  Modern organizations accumulate vast amounts of data, but they often fail to take full advantage of it because they struggle finding the right…

Towards long-term memory recall with Kinetica, an LLM, and contexts

By Chad Juliano | December 6, 2023

Prior to the emergence of machine learning, and particularly “deep learning,” I was an ML skeptic.  Judging from what I saw from the state of the art at the time, I’d say there was no way to program a CPU or a GPU — each of which, after all, is just a sophisticated instance of…

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