Apache Impala, introduced in 2012, revolutionized data analytics in the Hadoop ecosystem by enabling real-time SQL queries across massive datasets in HDFS and HBase, unlike traditional batch engines like MapReduce. Over the years, Impala has evolved significantly, gaining enterprise adoption by 2015 through enhanced security and integration capabilities. In 2017, it introduced support for Apache Kudu and leveraged Apache Arrow to boost performance with faster in-memory data transfers. By 2020, Impala became a critical component of modern data platforms, supporting ACID transactions, complex data types, and improved concurrency. Today, it is positioned as a cloud-ready analytics engine, offering Kubernetes-based deployment and hybrid cloud capabilities. Acceldata Pulse plays a crucial role in optimizing Impala's performance, providing tools for real-time query monitoring, historical trend analysis, and automated anomaly detection, thereby enhancing user experience with end-to-end query lineage mapping, resource utilization insights, and proactive alert systems. This enables data teams to address performance bottlenecks, reduce resolution times, and maintain service level agreements, transforming troubleshooting from reactive to proactive optimization.