What is your primary use case for Splunk Synthetic Monitoring?

In my organization, we have 150 to 160 applications yearly with different frameworks including .NET, Java, and Python based applications. All of them are hosted on different types of servers such as Windows, Linux, ECS, and EKS. With respect to deployments, we integrated Splunk Observability Cloud. Previously, we used Prometheus and Grafana. My organization considered Splunk Observability Cloud to be a premium side of observability, so they switched from our previous solution. We use the tracing feature in Splunk Observability Cloud.

My use case for Splunk Observability Cloud is primarily for monitoring and cloud management, and it serves us well.

I have been using Splunk Observability Cloud for the past one year in my career. Splunk Observability Cloud has been introduced to our project for end-to-end monitoring for applications, providing complete visibility of applications, services, tech stacks, and CIs, which constitutes the whole monitoring solution for an entire application. Previously, we were using different monitoring systems such as Dynatrace, the competitors of Splunk, and even Splunk Cloud Platform or Enterprise platforms for logging alone. Now we have the entire solution under one name and one platform, which is Splunk Observability Cloud, and that is why we mainly introduced Splunk Observability Cloud to our project.

I am using Splunk Observability Cloud as a log-based monitoring tool for my databases. We have ingested our database logs and OS system logs into Splunk Observability Cloud and are creating dashboards and alerting features over those alerts. One of my major use cases is that all kinds of databases I am currently working with have database logs that capture all information, warnings, and error messages. These database logs are moving to Splunk Observability Cloud. The first use case is that I no longer need to maintain a long list of flat files on my server for all those logs. Those can be directly ingested into Splunk Observability Cloud. The benefit I am seeing from here is that I can get pattern-based analysis of what kind of errors I am commonly getting and what the date patterns of those errors are. I can get dashboards over that and I can also create alerts. I can also incorporate those alerts with some back-end Git workflow for automatic remediation. This is one of the solutions. Another use case for Splunk Observability Cloud that we are seeing is that there are multiple times when there is a requirement to publish some kind of data. So instead of publishing an alert if those data breaches occur or if some kind of dashboard needs to be created, instead of sending data directly to the users, if that data is not PII, we are also ingesting that into Splunk Observability Cloud in a JSON format and then again, dashboards and other alerting can be created. These two are the main major use cases for which I am using Splunk Observability Cloud.

I work for a managed service provider, so I have different clients that require help in assessing various tools. I work with Splunk, ScienceLogic, and Nagios most frequently because I have small clients as well. We have Splunk Observability Cloud for some customers. The dashboards are good, and everything is nice, but unfortunately, it doesn't have long-term storage of the logs. So you need to use a data lake to store the logs. I would like to see agentless deployment and better integration with ticketing systems like ServiceNow, which is the biggest. We utilize the ability to enrich data with custom metrics in Splunk Observability Cloud to create tickets in ServiceNow. It is integrated with ServiceNow, but we enrich the tickets by putting the logs in the tickets and things of that nature, so it helps us. However, even that is a mixed approach. From Splunk Observability Cloud, you cannot put the logs directly in the tickets. Instead, it will create a ticket and send you an email with the logs. That integration could be improved.

My main use cases for Splunk Observability Cloud include retail analytics.

My primary use cases for Splunk Observability Cloud include creating dashboards for metrics, detecting incidents, and ensuring overall observability of applications, service connections, and integrations, along with reporting and Slack integrations.

My main use cases for Splunk Observability Cloud are indexing, dashboards, alerts, and reports.

My main use case is end-to-end monitoring for the application.

For the retail sector, we are building a solution for customer stores in order to know how the products are sold.

Our main use cases for Splunk Observability Cloud are to observe our application, our websites, and our infrastructure metrics.

My main use cases for Splunk Observability Cloud include Application Performance Monitoring, synthetic monitoring, and dabbling in infrastructure and what comes along with it; however, we do already have a tool that does infrastructure. We're debating about just switching it all over to Observability.

I use Splunk Observability Cloud for network logging analysis.

My main use case for Splunk Observability Cloud is end-to-end tracing of business processes.

My main use cases for Splunk Observability Cloud include Application Performance Monitoring, Real User Monitoring, and Synthetic Monitoring.

My primary use cases for Splunk Observability Cloud include alerting the business and the integrations app team, which are the largest users of Splunk within our company. They take up most of our ingest and have many alerts set up, along with log analysis and events analysis. Those are our biggest team users, and alerting in general plays a crucial role for incident creation across multiple teams, regardless of who the shareholders are, cutting across multiple teams.

My main use case for Splunk Observability Cloud is application monitoring.

Our main use cases include synthetic monitoring, APM, RUM, alerting, detectors, dashboards, and all related functionality.

My use case is the APM model, as we have the APM and since I mentioned I have all the flavors of Splunk, from Splunk Cloud, Splunk ITSI for specific event management, Splunk Observability Cloud is specific to the APM model. When I say APM model, it is infrastructure monitoring, synthetic transaction monitoring, and real user monitoring. With the help of Splunk Observability Cloud, I can monitor all the real-time transactions happening in the entire application. With the help of OpenTelemetry, I installed everything and got the logs, traces, and metrics. I created the dashboard since I have the data within the Splunk Observability Cloud. As I have the observability data within Splunk Observability Cloud, this observability cloud will not go with event management. If I want to go with event management, we will be sending the data to Splunk ITSI. Combining these, Splunk Cloud, Splunk ITSI, and Splunk Observability Cloud, we achieve the Splunk full-stack observability. This means I can monitor the overall application availability, including the REM, synthetics, all the APM, and the network observability coming from the arm. We can achieve full-stack observability with the help of all these Splunk stacks.

We are using the Splunk Observability Cloud for monitoring purposes and troubleshooting, and we are using that infrastructure in real time, in which we have infrastructure monitoring, application monitoring, log observer, and RUM synthetic monitoring. For troubleshooting purposes, we are installing the open telemetry collector agent on some of the servers, including Intel, Windows, and UNIX servers. I have also worked on the agent upgrade from version 0.103 to 0.1113, which is ongoing right now.

The solution involves observability in general, such as Application Performance Monitoring, and generally addresses digital applications, web applications, sites, and mobile applications. I worked with it in two companies: one in the energy sector and one in the hotel sector. The Splunk teams helped us with data collection, instrumentation, and many other options.

We primarily use Splunk Real User Monitoring to analyze performance bottlenecks and application transactions. It allows us to see how applications are experienced on the user side, making it easy to capture any bottlenecks or performance issues.

Splunk is primarily used for log monitoring, where I collect all my security logs, system logs, and application logs into a centralized place. This helps me customize my monitoring models.

Typically, the standard approach for Splunk sizing involves gathering data from the entire IT environment, regardless of whether it's hardware, virtualized, or application-based. This data is then collected and monitored through Splunk as a comprehensive security solution. We also work with Splunk-related platforms like Application Performance Monitoring to provide a holistic view of system performance. Recently, we implemented this solution for a bank in Jetar. Splunk excels at collecting high-volume data from networks, making it ideal for performance monitoring and scaling. During the sizing process, it's crucial to calculate the daily data ingestion rate, which determines the amount of data Splunk Enterprise needs to process and visualize for security purposes. Several factors need consideration when sizing Splunk: tier structure hot and cold buckets, customer use cases for free quota access, and storage choices based on data access frequency. Hot buckets typically utilize all-flash storage for optimal performance and low latency, while less frequently accessed data resides in cold or frozen buckets for archival purposes. In essence, the goal is to tailor the Splunk solution to meet the specific needs and usage patterns of each customer. One challenge that our customers face is slow data retrieval. Customers may experience delays in retrieving call data due to complex search queries within Splunk Enterprise Security. These queries can sometimes take up to an hour and a half to execute. Our architecture incorporates optimized query strategies and customization options to significantly reduce data retrieval times. This enables faster access to both hot and cold data. Another challenge is scalability constraints. Traditional solutions may have limitations in scaling to accommodate increasing data volumes. This can be a significant concern for customers who anticipate future growth. Our certified architecture is designed for easy and flexible scalability. It allows customers to seamlessly scale their infrastructure based on their evolving needs, without encountering the limitations often faced with other vendors' solutions. The final challenge is complex sizing and management. Traditional solutions often require extensive hardware configuration and sizing expertise, which can be a challenge for many organizations. This reliance on hardware expertise can hinder scalability and adaptability. Our architecture focuses on software and application administration, minimizing the dependence on specific hardware configurations. This simplifies deployment and ongoing management, making it more accessible to organizations with varying levels of technical expertise. Our architecture leverages Splunk's native deployment features, including: Index and bucket configuration. Data is categorized into hot, warm, and cold buckets for efficient storage and retrieval. Active/passive or active/active clustering. This ensures high availability and redundancy for critical data. Resource allocation. Data, compute, and memory resources are distributed evenly across clusters for optimal performance. For high-volume data ingestion exceeding 8 terabytes per day, we recommend deploying critical components on dedicated physical hardware rather than virtual machines. Virtualization can introduce overhead and latency, potentially impacting performance. Utilizing physical hardware for these components can help mitigate these bottlenecks and ensure optimal performance for large data volumes.