This study explores the optimization of Databricks clusters from both empirical and theoretical perspectives. Through a comprehensive survey of developers at a Chilean consulting firm, the research identifies key challenges and priorities in configuring and optimizing Databricks clusters for efficient data processing. The survey results highlight the critical areas of execution times for data processes and the efficient use of IT resources, emphasizing the necessity for focused optimization strategies. Despite Databricks’ widespread adoption in data science and analytics, there is a notable absence of dedicated benchmarking and optimization proposals targeting Databricks specifically. This research aims to expose this gap for future applications, based on the literature review and industry insight gathered through the survey. The results highlight the importance of cluster configuration for cost-effective and efficient data processing, ultimately improving the performance and cost-effectiveness of data analysis operations.