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A ChatGPT-based interface converts natural language questions into structured query language, allowing users to easily explore large, complex datasets.
An SQL analytics database platform developer has announced what it calls “conversational querying” of an organization’s proprietary databases.
Kinetica, whose high-speed analytics cloud platform for big data is used across the public sector, has developed a ChatGPT-based front end to its database that converts users’ natural language questions into structured query language, allowing users without SQL experience to quickly and intuitively analyze large, complex datasets.
With the company’s SQL-ChatGPT integration, data analysis can be conducted in natural language in the Kinetica Cloud. Rather than writing and debugging complex SQL queries, users can ask conversation-style questions, and ChatGPT delivers answers in seconds based on its knowledge base and understanding of the user's intent. If required, users can then ask follow-up questions or provide additional context.
The rapid chat-to-SQL conversion is facilitated by what Kinetica calls its GPU-powered native vectorization. “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,” company officials explained in the announcement. The parallel processing allows concurrent users to work with large volumes of data quickly and efficiently.
Additionally, because Kinetica ingests streaming data in real time and offers several analytics modes—such as time series, spatial, graph and machine learning—the type of conversational questions ChatGPT can answer is much broader. Corporate users could ask about real-time stock inventories or improving customer experience in different locations and at different times of the year.
Among the benefits the company cited were the ability for a wider range of users to query their organization’s data, ultimately leading to more data-driven decisions. Immediate answers to conversational queries save time and improve overall efficiency, Kinetica officials said. The tool might also help users discover correlations and patterns in their data that may have not been apparent or too difficult to find.
"While ChatGPT integration with analytic databases will become table stakes for vendors in 2023, the real value will come from rapid insights to complex ad-hoc questions," Kinetica CEO and co-founder Nima Negahban said. "Enterprise users will soon expect the same lightning-fast response times to random text-based questions of their data as they currently do for questions against data in the public domain with ChatGPT."
The company is offering the ChatGPT integration at no cost in its free developer edition, noting that the developer edition can be installed on a local machine and run as a Docker container.