OpenText Real User Monitoring vs VMware Tanzu Observability by Wavefront comparison

Real User Monitoring (RUM) an End user monitoring that gives you visibility into user behavior for fast, targeted problem resolution. It monitors the performance and availability of business-critical application services for all users at all locations all the time. It automatically discovers underlying infrastructure and classifies user actions - giving you instant visibility into session and whole service health over web, cloud, and mobile user experience. It allows you to trace user experience across tiers, capture live sessions, see where customers clicked, measure response times, and see pages that caused problems. And you can easily capture and replay user sessions to create test scripts that reflect real user behavior. All this data gives you new ability to analyze which application transactions your users are performing and what application response they are experiencing. RUM currently supports over 20 application protocols and applications such as SAP, Citrix, and native mobile application monitoring on Android.

VMware Tanzu Observability is a high-performance streaming analytics platform. It enables observability for metrics, counters, histograms, and traces/spans. Because it scales to very large data ingestion rates and query loads, the product is unique in its field. You can collect data from a variety of services and sources across your entire application stack, as well as examine details for earlier ingested data. After sending your data to your Wavefront instance, you may examine your data on custom dashboards, receive alerts on problem values, and do anomaly detection and forecasting.

VMware Tanzu Observability can support over 200 integrations, including cloud providers, DevOps tools, big data, and others. You can utilize the platform’s sophisticated Graphical User Interface to connect with the Wavefront service, which includes several pre-built dashboards, charts, and alerts. SDKs, the Wavefront REST API, and CLIs are also accessible on our Github website. Additionally, there are tight integrations with Spring Boot, Kubernetes, and Tanzu Mission Control.

VMware Tanzu Observability Features

VMware Tanzu Observability has many valuable key features. Some of the most useful ones include:

• Analytics: Advanced query language helps you save time and money.

• API: API with a lot of flexibility for effective alerting and visualization.

• Architecture: Enterprise-scale performance in real time.

• Dashboards and charts: Use dashboards and charts to visualize the data. Filters and functions can help you find exactly what you're looking for. Use aggregation functions like sum() or average to combine multiple time series. Use dashboard variables to adjust the focus of a dashboard or combine functions to show or hide some of the information. A variable, for example, could be used to display the production or development environment. Choose from a variety of chart types (line plot, point plot, table, etc.)

• Alerts: You can build alerts directly from charts and customize them to detect problems. The solution will send you alert notifications with details and a chart graphic if you've set up an alert with customized information.

• Queries: You can extract exactly the information you need using the Wavefront query language (WQL). You can personalize your charts with filters and functions so that the signal stands out among the noise.

• Distributed Tracing tool: Use Distributed Tracing to interact with a service map, inspect traces and spans, and drill down into problematic areas.

• Various setup methods:
  
  
• Cloud: Perform the bare minimum of configuration to allow the Wavefront service to access data in your cloud environment. As a result, data from cloud services can be directly ingested.
  
  
• Integrations: VMware Tanzu Observability supports more than 200 integrations with different data sources. You just need to change a single configuration file and you're ready to go.
  
  
• Start from where you are: If your environment already has a metrics infrastructure, you can pre-process the data to match the Wavefront Data Format and transmit it to the Wavefront proxy directly.
  
  
• Direct Ingestion: Direct ingestion is the best option in some circumstances.
  
• Histograms: Wavefront histograms may be the best answer for high-velocity measures.
  
• App monitoring with distributed tracing: For traces, the product supports Jaeger and Zipkin, as well as any OpenTracing-instrumented applications. You can also use one of our SDKs to transmit custom traces. The Application Map GUI makes it simple to explore trace data, RED metrics, and other metrics.