eBPF, or Extended Berkeley Packet Filter, is a transformative technology for modern observability and security, allowing programs to run within the operating system's kernel space without altering the kernel source code. Originally designed for networking tasks, eBPF now has broad applications across security and observability domains, offering a less invasive alternative to traditional Application Performance Monitoring (APM). By reducing the need for manual instrumentation and minimizing overhead, eBPF enables safer and more efficient data collection directly within the kernel, allowing for data aggregations and summaries to be passed to user-level applications. The BPF Compiler Collection (BCC) simplifies the implementation of eBPF programs, making it accessible for developers to trace system-level events and troubleshoot issues that traditional tools struggle to address. Despite some limitations, such as the inability to safely modify data or dynamically add tracing IDs, eBPF is poised to complement existing APM solutions by enhancing performance and providing deeper insights into system operations. With its potential to integrate with machine learning models for proactive problem detection, eBPF is expected to play a significant role in the evolution of observability solutions, such as Elastic, in the future.