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Kinetica Analytical Database With ChatGPT
Focus: ChatGPT converts natural language questions into SQL queries
In May Kinetica said that it had integrated its high-performance analytics database with ChatGPT, making it among the first to use the popular AI technology for “conversational querying” that converts natural language questions into Structured Query Language queries.
Kinetica develops a real-time, vectorized database for analyzing and observing huge volumes of streaming time series, spatial and graph data. The use of ChatGPT makes it possible for users to ask complex questions of proprietary, complex data sets in the Kinetica system and receive answers in seconds, according to the Arlington, Va.-based company.
SQL is the standard development language for building queries for retrieving data in relational databases. But with the exception of some “power users” who can develop their own SQL queries, SQL-based queries are largely developed by programmers and data engineers either for prebuilt queries and reports or in response to business users’ requests—all limitations on generating timely value from data.
The ChatGPT interface improves the ease of use of the Kinetica database by providing a way for users to directly ask ad hoc questions of proprietary data in a database system—even complex queries not previously developed—and receive answers in seconds without the need to pre-engineer data. The new capabilities also boost productivity by providing real-time access to information and offers the ability to more quickly identify patterns and insight within the data.