Welcome everyone to our discussion. This is Ra Pala. Today we'll be discussing the topic, mastering observability and distributed Systems. This is an important topic for analytics leaders navigating complex environments. Today we will explore the strategic approach to achieving true visibility, generating meaningful insights, and driving impactful action across your distributor systems. The key is developing a comprehensive observability strategy that goes beyond just monitoring, it's about unlocking the full potential of your idea. To drive business outcomes, we will dive into the critical capabilities required and share our real world examples of how leading organizations are putting observability into practice, right? By the end I'm sure you'll have a clear roadmap for elevating observability as a strategic imperative within your own organization. So let's get started. Let me dive into slide number two here. So the business case for observability is clear. It delivers faster incident resolution. Cost savings and improved customer satisfaction. Observability helps reduce meantime to resolution by 73%, allowing us to identify and fix issues more quickly. It also leads to 42% cost savings by optimizing our infrastructure spend and reducing waste. Most importantly, observability drives a 91% increase. And customer satisfaction through better user examples or experiences. A 99.99% availability targets translates to 22 minutes of allowable downtime per month. As you see in the picture right here, these metrics demonstrate the tangible benefits of investing in observability for our organization. With observability, we can proactively monitor our systems, rapidly, troubleshoot our problems, and continuously improve the services we deliver to our customers and clients across the world. As you look at this slide, traditional monitoring focuses on known issues with predefined metrics and threshold based alerts. This is reactive approach. In contrast, modern observability aims to uncover unknown unknowns and through high cardinality telemetry and context rich insights. This allows for more proactive troubleshooting. The key differences are that observability deals with the unknown, uses more comprehensive data, and provides deeper context to drive faster problem solving, right? Observability gives you a more complex. Complete picture of your systems, allowing you to be proactive rather than just reacting to the issues. With observability, you gain a more, complete picture of your systems, allowing you to be proactive, like I mentioned, rather than just reacting to the issues. And also with observability, you can gain visibility into the unknown and unknowns that traditional monitoring may miss leading to a better overall system health and performance output. This slide. As you can see, observability is a key capability that allows us to understand the health and performance of our systems and applications. Again, it provides three main benefits. Basically business insights giving us visibility into the user journeys and behaviors to inform product decisions. Operational intelligence, helping us detect patterns and anomalies in systems behavior to quickly identify and resolve issues. Telemetry, foundation, essentially providing the underlying data, sources of traces, logs, and metrics that power the other two areas. Observability is not just a technical capability, but also a strategic data product that can deliver value across the organization. All right. As you can see, we have there are four pillars of observability. They are the key data sources that provide visibility to the health and performance of a system. Metrics gives us the quantitative measurements over time, like CPU, memory, latency and throughput as you can see logs provide detailed event records with timestamp and structured data process. Show the request flows across the services, helping us improve performance bot. Whereas events capture the state changes and transitions like deployments and configuration changes together, these four pillars gives us a comprehensive view into the behavior and operating of our systems. Now observability is critical for understanding complex systems and identifying issues before they impact the business. Analytics capabilities, like interactive data exploration, machine learning driven anomaly detection, and predictive insights can significantly enhance observability. Data exploration allows you to query across all your telemetry data to uncover hidden patterns and trends. Pattern recognition uses machine learning can automatically detect anomalies that may indicate emerging problems. Correlating business and technical data provides important context to understand the impact of issues. Predictive analytics can forecast potential issues before they actually occur, allowing you to get ahead of the problems. These analytics capabilities gives us a deeper, more proactive observability into your own systems and processes. Alright, now this slide outlines the key components of a scalable observability architecture and also the infrastructure. The instrumentation layer includes tools like Open Telemetry agents and SDKs to capture observability data from applications and infrastructure. The collection and transport layer, ingest the data using technologies like, excuse me, Kafka, Panis, and Flint. The storage and processing layer stores the data in time series databases and distributed tracing systems. Finally, the analysis and visualization layer, as you can see, provides dashboards, notebooks, and alerting to make sense of observability data. Each of these layers plays a critical role in building an end-to-end observability pipeline that can scale to handle growing volumes of data. In your systems. As you can see, the tooling landscape for observability and monitoring is broad with many different tools and technologies available. This slide provides a gallery of some of the major players in the space, including. PROEs Grafana Elastic Search Open Telemetry, Jagger, Datadog, new Relic, and Dynatrace. These tools cover a range of capabilities from metrics collection and visualization to distributing, tracing to full stack observability platforms. When choosing tools for your observability stack, it's important to evaluate the specific needs of your organization and architecture and how well each tool fits those requirements, right? Some key factors to consider are ease of use, scalability, integrations, and oral cost and complexity. The right combination of tools can provide deep visibility into the health and performance of your systems, enabling faster, troubleshooting, and better patient making right now. Rolling onto the next slide. This slide outlines for common pitfalls that organizations offer encounter when managing their IT operations and monitoring systems. The first pitfall is data deluge collecting vast amounts of data without a clear purpose. Leading to an overall overwhelming signal to noise ratio that makes it difficult to extract meaningful insights. The second pitfall is Tools Pro, having a fragmented visibility across multiple platforms, making it challenging to correlate and connect the data. The third pitfall is SLO ownership. Where the platform teams work in isolation, limiting the cross-functional insights and collaboration needed to effectively address issues. The fourth pitfall is alert fatigue where teams are bombarded with too many notifications, many of which are low value and disruptive, leading to a lack of focus on the critical issues. These are the common challenges that organizations need to be aware of and proactively address to improve their IT operations and monitoring capabilities. Moving on to the slide number 10 here. Observability is a key capability that organizations need to measure over time. This slide outlines a four stage maturity roadmap for observability. At the reactive stage, we are simply responding to S after they have already, being impacted by the business. The proactive stage is about identifying issues as they emerge before they cause major disruptions. In the analytical stage, we gain a deeper understanding of patterns and dependencies in our systems. The ultimate goal is to reach the predictive stage where we can forecast issues before they even occur. Going to the next slide. As you see most of the analytics leaders, our role is to bridge the gap between the technical and business domains, right? Translating the telemetry and insights as we gather into the tangible business impact. And we need to architect the data driven feedback loops that correct the insights we uncover to meaningful action and change within the organization. As championing cross-functional collaboration is a key here. Breaking down silos between the teams and getting everyone aligned around a shared understanding of the data is a very key point here. Ultimately, our goal is to drive a data literacy and culture empowering everyone in the organization to leverage insights as we surface to make better and more informed decisions. The sliding material here is basically about the key takeaways from our discussion today for driving value from observability. First, we need to clearly define the business outcomes we want to achieve and link our observability efforts to those metrics. Second. We should start small by prior prioritizing high impact services, then scale our observability capabilities incrementally over time. Building cross-functional teams that blend analytics and operations expertise will be crucial for driving maximum value. And the finally, the last STA is we need to evolve our observability. Maturity, step by step following the clear roadmap to reach our desired future state. So these steps will help ensure our observability investments deliver meaningful business impact across the platforms. And this last slide for today as part of the session, thank you for all joining us today. Exploring how analytics drives observability maturity. We appreciate your time engagement throughout this presentation as you continue on your observability journey. Please don't hesitate to reach out if you have any questions. We are always here to support you. Let me know if there's anything else I can do to help. As you work to advance your observability capabilities. Thank you all and thank you Keff 42 for giving me this opportunity to walk through the observability topic. Thank you. I.