Digital transformation, in part accelerated by the COVID-19 pandemic, has driven rapid adoption of cloud-native technologies such as microservices and Kubernetes over the last two years.\n\nThese modern application architectures offer huge benefits for organisations in terms of improved speed to innovation, greater flexibility and improved reliability.\n\nBut many IT teams are now finding themselves under immense pressure as they attempt to monitor and manage availability and performance across hugely complex cloud-native application architectures. They\u2019re struggling to get visibility into applications and underlying infrastructure for large, managed Kubernetes environments running on public clouds.\n\nWithout doubt, staying on top of availability and performance is far more challenging in a software-defined, cloud environment, where everything is constantly changing in real-time. But digital transformation projects and innovation initiatives continue to run at break-neck speed, the heat is on for technologists to adapt and get the visibility and insight they need across these modern environments.\n\nA question of scale\n\nTraditional approaches to availability and performance were often based on physical infrastructure. Flash back 10 years, and IT departments operated a fixed number of servers and network wires - they were dealing with constants and fixed dashboards for each layer of the IT stack. The introduction of cloud computing added a new level of complexity, organisations found themselves continually scaling up and down their use of IT, based on real-time business needs.\n\nWhile monitoring solutions have adapted to accommodate rising deployments of cloud alongside traditional on-premise environments, the reality is that most were not designed to efficiently handle the dynamic and highly volatile cloud-native environments that we increasingly see today.\n\nIt\u2019s a question of scale\u2026 these highly distributed systems rely on thousands of containers and spawn a massive volume of metrics, events, logs and traces (MELT) telemetry every second. And currently, most technologists simply don\u2019t have a way to cut through this crippling data volume and noise when troubleshooting application availability and performance problems caused by infrastructure-related issues that span across hybrid environments.\n\nIntroducing cloud-native observability\n\nThis is why it\u2019s now so essential for technologists to implement a cloud-native observability solution, to provide observability into highly dynamic and complex cloud native applications and technology stack.\n\nIn order for technologists to be able to properly understand how their applications are behaving, they need visibility across the application level, into the supporting digital services (such as Kubernetes) and into the underlying infrastructure-as-code (IaC) services (such as compute, server, database and network) they leverage from their cloud providers.\n\nBut before rushing to implement a solution to this growing challenge, there are some important factors for technologists to consider when thinking about observability into cloud environments:\n\nFirstly, technologists should be looking to implement a purpose-built solution which can observe distributed and dynamic cloud native applications. Traditional monitoring solutions continue to play a vital role - and will do so for years to come - but it becomes problematic when cloud functionality is bolted onto existing monitoring and APM solutions. Too often, when new use cases are added to existing solutions, data remains disconnected and siloed, forcing users to jump from tab to tab to try to identify the root causes of performance issues. Very few of these solutions provide complete visibility, for example insight into business metrics or security performance, and many are naturally biased towards a particular layer of the IT stack depending on their legacy, whether that is the application or core infrastructure.\n\nNew teams require new approaches\n\nCloud-Native applications are built in completely different ways and they\u2019re managed by new teams - Site Reliability Engineers (SRE), DevOps and CloudOps - with new skill sets, mindsets and ways of working. Therefore they ask for a completely different kind of technology to track and analyse availability and performance data. They need a solution that is truly customised to the needs of cloud-native technology stack to decipher short-lived microservices interactions with one another and which can be long gone once troubleshooting is done.\n\nSRE and DevOps teams need a solution that embraces open standards, giving a full-stack, correlated view of all telemetry data across the technology stack - most notably, Open Telemetry. Technologists need to be able to collect all telemetry across the stack and domains, and then analyse all of that telemetry data - since it is interconnected and interdependent - at once. A standards-driven solution is essential to future-proof organisations for the next decade and beyond.\n\nTechnologists also need a solution that allows them to monitor the health of key business transactions that are distributed across their technology landscape. If an issue is detected, they need to follow the thread of the business transaction\u2019s telemetry data, so they can quickly determine the root cause of issues, with fault domain isolation, and triage the issue to the correct teams for expedited resolution.\n\nFinally, technologists should be looking for a solution that combines observability with advanced AIOps functionality. They need to leverage the power of AIOps and business intelligence to prioritise actions for their cloud environments. In the future, organisations will utilise AI-assisted issue detection and diagnosis with insights for faster troubleshooting. Ultimately, it allows technologists to focus more quickly on what really matters, where and why it happened.\n\nThe application world has evolved massively over the past two years and technologists need to ensure that their monitoring capabilities keep pace. From understanding how highly-distributed cloud-native applications work and predicting incidents, to adopting new ways to gather vast amounts of MELT telemetry data, teams across IT Ops, DevOps, CloudOps, and SREs need contextual insights that provide business context deep within the tech stack.\n\nOnly with the right cloud-native observability solution in place, will organisations be able to maximise the benefits of modern applications, driving enhanced digital experiences for customers and improved business outcomes.