  Serverless query processing has become increasingly popular due to its
advantages, including automated hardware and software management, high
elasticity, and pay-as-you-go pricing. For users who are not system experts,
serverless query processing greatly reduces the cost of owning a data analytic
system. However, it is still a significant challenge for non-expert users to
transform their complex and evolving data analytic needs into proper SQL
queries and select a serverless query engine that delivers satisfactory
performance and price for each type of query.
  This paper presents PixelsDB, an open-source data analytic system that allows
users who lack system or SQL expertise to explore data efficiently. It allows
users to generate and debug SQL queries using a natural language interface
powered by fine-tuned language models. The queries are then executed by a
serverless query engine that offers varying prices for different service levels
on query urgency. The service levels are natively supported by dedicated
architecture design and heterogeneous resource scheduling that can apply
cost-efficient resources to process non-urgent queries. We envision that the
combination of a serverless paradigm, a natural-language-aided interface, and
flexible service levels and prices will substantially improve the user
experience in data analysis.
