The best features of Splunk Observability Cloud include the Splunk Assistant and the Splunk AI Assistant, which help in triaging issues and identifying them immediately, allowing me to create dashboards in a matter of some minutes or seconds. We can monitor all the Kubernetes, Docker, and whatever platform we have, including AWS, Azure, and GCP. We can monitor all the AWS CloudWatch logs, GCP stacks, and storage. Everything can be monitored because we have the out-of-the-box dashboard add-ons. You don't need to do anything, but you might have to work a little bit; however, everything might be inbuilt, allowing me to fetch everything and monitor from all platforms, whether private cloud, public cloud, or data centers like Equinix. The benefits I can see from using Splunk Observability Cloud include faster and quick resolution, allowing me to identify the root cause analysis, or RCA, for whatever issue is currently going on. This is the first analysis and first RCA. That is one of the most significant advantages or features we can achieve with the help of observability, offering simple and fast resolution. Splunk Observability Cloud has indeed helped reduce downtime, as it has inbuilt operations that send triggers in real time, even for false alerts or positives, within seconds. It can identify anomalies and future forecasting. If an anomaly continuously occurs, it indicates something has gone wrong. It may not be a real issue, but something is about to happen. In such cases, we raise an incident or utilize the ITSM tool integration to immediately inform the next dealer group, ensuring quick action is taken. Regarding blind spots, I haven't seen significant issues in this aspect with Splunk Observability Cloud. However, if one wants to eliminate blind spots, it may require writing some technical mix, which I haven't explored in depth. In terms of AI-powered analytics and guidance provided by Splunk, we have two AI features: the Splunk AI Assistant and the Splunk Commander AI. The Commander AI embodies a generative AI functionality, connecting with LLMs. According to LLM standards, various algorithms are written to perform specific functions. As soon as the request comes from Splunk, it connects with the respective LLM, which will select the algorithms you need. This is an inbuilt AI module within Splunk, and the only requirement is a specific add-on called the Splunk MLTK, which stands for Machine Learning Toolkit. Having this MLTK add-on, tightly coupled with AIOps capabilities and ML, yields good results in AIOps operations. Regarding the effectiveness of the out-of-the-box customizable dashboards provided by Splunk Observability Cloud, they showcase IT performance very well. It includes multiple add-ons and intelligent integrations, but it is crucial to ensure that data comes to Splunk Observability Cloud via the OTel Collector and universal forwarder. Once data is received, I can create dashboards for GCP utilization or AWS CloudWatch details. Although there may be numerous metrics to monitor from AWS and GCP, not all are critical, so the dashboards have been constructed to focus on the critical parameters we want to monitor. In my organization, we have built observability for many applications. Although I cannot disclose specific account information, I use it for development purposes with a minimum number of users. User numbers can extend based on infrastructure, environment size, and data ingestion rates. The maintenance of Splunk Observability Cloud is very easy; it's manageable.
