Recent attendance at the Hadoop and Cassandra Summits revealed several advanced performance challenges faced by users of these Big Data technologies. In Hadoop, issues such as data locality problems, inefficiencies in job code, TaskTracker slowdowns, and NameNode or DataNode performance degradation were highlighted. These challenges are exacerbated by the distributed nature and sheer scale of Hadoop clusters, making them difficult to diagnose and resolve. Solutions such as advanced monitoring, baselining, and adjusting data placement or scheduler configurations are essential for improving performance. Similarly, Cassandra users experience performance degradation over time due to read time issues linked to data spread across multiple SStables and improper data access patterns. The need for baselining to detect performance changes and using token-aware clients to bypass coordinator issues was emphasized. Both technologies, while scalable, are not immune to performance inefficiencies, highlighting the ongoing need for expertise in diagnosing and optimizing these complex systems.