Hi everyone, 
Our team has recently adopted Hue for data visualization and querying within our company’s extensive big data environment. While we’ve found the platform incredibly valuable, we’re encountering performance challenges as our data volume grows.
I’m reaching out to the community for expert advice on optimizing Hue for large-scale data processing. We’re particularly interested in:
- Configuration best practices: Are there specific Hue settings to enhance performance with massive datasets?
- Resource allocation: How can we effectively distribute resources (memory, CPU) for optimal Hue operation?
- Query optimization techniques: What strategies can we employ to efficiently handle large-scale data queries?
- Complementary tools: Are there any recommended integrations or tools to boost Hue’s performance?
- We’re utilizing Hue on a Hadoop ecosystem for complex queries and interactive visualizations. Any insights, documentation, or real-world examples would be immensely helpful.
I also check this: https://discourse.gethue.com/t/hue-pyspark-connector-using-livy-how-to-change-spark-driver-memorlooker But I have not found any solution. Could anyone provide me the best solution for this?
Thank you for sharing!
Respected community member 