Conversational Query

While LLMs like ChatGPT can convert natural language to SQL, the speed of response for data analytics questions is still dependent on the underlying data platforms being used.

Conventional analytic databases require extensive data engineering, indexing and tuning to enable fast queries, which means the questions must be known in advance. If the questions are not known in advance, a query may take hours to run or not complete at all.  

In a vectorized query engine, data is stored in fixed-size blocks called vectors, and query operations are performed on these vectors in parallel, rather than on individual data elements. This allows the query engine to process multiple data elements simultaneously, resulting in radically faster query execution on a smaller compute footprint. Vectorization is made possible by GPUs and the latest advancements in CPUs, which perform simultaneous calculations on multiple data elements, greatly accelerating computation-intensive tasks by allowing them to be processed in parallel across multiple cores or threads.

Kinetica vectorized columnar architecture enables convergence of multiple modes of analytics such as time series, spatial and graph that broadens the types of questions that can easily be answered, such as, "How can we improve the customer experience considering factors such as seasonality, service locations and relationships?"

Kinetica is able to ingest massive amounts of streaming data in real-time to ensure answers represent the most up to date information, such as, “What is the real-time status of our inventory levels and should we reroute active delivery vehicles to reduce the chances of products being out of stock?

There is no additional cost for the ChatGTP integration in Kinetica. Users can experience this feature for free on Kinetica Cloud or Dev Edition.

Kinetica uses the “gpt-3.5-turbo” model