Next-generation observability platforms: redefining debugging and monitoring at scale

Software systems globally have become increasingly complex, and conventional monitoring and debugging tools are not sufficient for them. The baselines are synthesized from general metrics and general rules, at the very least these are not sufficient in order to give a high level of understanding in the form of the distributed dynamic environment. To solve this issue, solution platforms based on the concept of observability, which use platforms of advanced capabilities (e.g., real-time data analysis, artificial intelligence (AI) analytics, and automated anomaly detection) have been created. Solutions are described through present-day real-time stream processing, Artificial intelligence (AI) analytics, automated anomaly detection, etc, to allow prediction of degradation and system optimization before degradation events occur. These webs give an all-round overview of system performance, involving logs, metrics, traces, and events and as a result, companies can get better reliability, decrease downtime, and improve operational efficiency. Nevertheless, the benefits are accompanied by limitations, including data quality limitations, economies of scale limitation, and security limitations. In this paper, the evolutions of the platforms of observability, their functionalities, the value, the limitations, and the good practices for their successful implementation are traced along with suggestions on the future evolutions of the industry.