Polars Cloud aims to address various pain points within the data processing ecosystem by introducing a dynamic 'diagonal scaling' approach, allowing flexibility between horizontal and vertical scaling based on specific query characteristics. This flexibility addresses real-world data workloads that often have mixed requirements. Users are particularly interested in how Polars' new streaming engine, leveraging out-of-core processing, will perform in comparison to Dask, which has faced challenges in widespread adoption despite its capabilities. The unified API approach proposed by Polars is expected to alleviate the cognitive burdens that many face when switching between pandas for local and PySpark for distributed jobs, enhancing productivity. Many users expressed interest in participating in early access and are keen to see benchmark comparisons against competing technologies like Ray and Spark. Concerns about catalog support, self-hosting capabilities, and potential barriers to switching for companies entrenched in existing solutions like Spark were also highlighted. Overall, excitement is palpable for the Polars team, especially as they create a competitive alternative in the data processing landscape. However, users remain wary about cloud usage fees for big data processing and integration with existing services like AWS Glue.