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Synthetic tests from Endpoint devices on a scheduled basis, providing continuous performance monitoring even when the user is not active.The Hercules release includes many improvements and fixes aimed at making Endpoint Monitoring easier to use. One such improvement is the ability to generate alerts based on endpoint CPU and RAM usage.Other Endpoint improvements and bug fixes include increasing the maximum number of Endpoints per test to 65,000 and a network Sankey diagram that now shows all network devices by default. There are also additional improvements aimed at making it easier to view and act on your Endpoint data efficiently.ELK Stack (Elasticsearch, Logstash, and Kibana) IntegrationWe have an extensive list of integrations and we’re happy to announce that ELK stack is now joining our ecosystem of technical partners.As anyone working with it knows, [ELK Stack]( stack&gclid=EAIaIQobChMI-Nva7YW98gIVzNvVCh2RewchEAAYASAAEgIOEfD_BwE) is a fantastic way to help process big data. It’s a distributed, free, and open search and analytics engine for all types of data, including textual, numerical, geospatial, structured, and unstructured. It comprises three open-source products: Elasticsearch, Logstash, and Kibana.With Hercules, we are excited to be introducing two methods of integration with ELK Stack:Catchpoint pushes data to ELK Stack – This method uses Catchpoint's Test Data Webhook API to send data directly to Elasticsearch. Each time a Catchpoint test runs with the Test Data Webhook enabled, the results are pushed to Elasticsearch via a public-facing endpoint accessible over http or https.ELK Stack pulls data from Catchpoint – This integration relies on Logstash to pull test data from Catchpoint’s REST API. Logstash dynamically ingests and transforms the data using csv filters, and then ships it to the Elasticsearch engine.Once Elasticsearch has received the Catchpoint data by either method, you can apply visualizations and perform data analysis using Kibana.Octopus Deploy IntegrationOctopus Deploy is an automated deployment and release-management tool used by leading continuous-delivery teams worldwide. We’re happy to let you know that DevOps teams can now configure Octopus Deploy to register each successful deployment as an Event in Catchpoint. This adds a marker in the Catchpoint Portal so that you can see how changes to your application correlate with changes in the user experience.Learn More TodayIf you are interested in testing out Catchpoint, check out our Guided Test Drive.All of us here at Catchpoint are passionate about continuously innovating and improving our product to make our customers’ lives better. Part of this process involves regular product releases

Easier to use. One such improvement is the ability to generate alerts based on endpoint CPU and RAM usage.Other Endpoint improvements and bug fixes include increasing the maximum number of Endpoints per test to 65,000 and a network Sankey diagram that now shows all network devices by default. There are also additional improvements aimed at making it easier to view and act on your Endpoint data efficiently.ELK Stack (Elasticsearch, Logstash, and Kibana) IntegrationWe have an extensive list of integrations and we’re happy to announce that ELK stack is now joining our ecosystem of technical partners.As anyone working with it knows, [ELK Stack]( stack&gclid=EAIaIQobChMI-Nva7YW98gIVzNvVCh2RewchEAAYASAAEgIOEfD_BwE) is a fantastic way to help process big data. It’s a distributed, free, and open search and analytics engine for all types of data, including textual, numerical, geospatial, structured, and unstructured. It comprises three open-source products: Elasticsearch, Logstash, and Kibana.With Hercules, we are excited to be introducing two methods of integration with ELK Stack:Catchpoint pushes data to ELK Stack – This method uses Catchpoint's Test Data Webhook API to send data directly to Elasticsearch. Each time a Catchpoint test runs with the Test Data Webhook enabled, the results are pushed to Elasticsearch via a public-facing endpoint accessible over http or https.ELK Stack pulls data from Catchpoint – This integration relies on Logstash to pull test data from Catchpoint’s REST API. Logstash dynamically ingests and transforms the data using csv filters, and then ships it to the Elasticsearch engine.Once Elasticsearch has received the Catchpoint data by either method, you can apply visualizations and perform data analysis using Kibana.Octopus Deploy IntegrationOctopus Deploy is an automated deployment and release-management tool used by leading continuous-delivery teams worldwide. We’re happy to let you know that DevOps teams can now configure Octopus Deploy to register each successful deployment as an Event in Catchpoint. This adds a marker in the Catchpoint Portal so that you can see how changes to your application correlate with changes in the user experience.Learn More TodayIf you are interested in testing out Catchpoint, check out our Guided Test Drive.This is some text inside of a div block.

This case and organizations may choose to have a VPN set up to mitigate this issue only to a certain extent.With all the complexities involved, it becomes extremely important to monitor the health of the network to ensure seamless reachability to different applications (be it SaaS or internal app) and connectivity within an organization.Recently, one of our larger enterprise customers reached out to us with a problem, the employees of the organization noticed that accessing a specific application to download files took longer than usual and they were unable to identify the root cause.The office network infrastructure is set up in such a way that any traffic is set to follow one of the 3 routes through:MPLSSD-WANOpen InternetTo analyze the issue, the first step we took was to deploy the Catchpoint Enterprise Nodes to help identify exactly where the slowness was noticed. The node served as checkpoints in our customer’s network infrastructure and monitored the performance and availability of intranet and SaaS applications used by employees.We then set up synthetic tests to monitor the web application, the data indicated that the slow Load times were seen across MPLS and SD-WAN when compared to the open internet line. In the chart below, the Load time varies across different network types.Image4: Data from MPLS, SD-WAN, and DIA set upAnalyzing the data from the Enterprise nodes, the customer was able to determine, for certain, that the issues were local to the sites. After further analysis of the different network components, changes to the local firewall configuration helped improve the Load time.Monitoring within the enterprise network is key to maintaining employee experience. Consider another example, Catchpoint Enterprise nodes were able to identify problems within the SD-WAN network. The Sankey chart below illustrates the issue.Image5: Sankey ChartIn this case, the enterprise nodes were deployed across multiple branch offices (shown on the left side of the Sankey chart), from where the employees access an application used for collaborating with other teams within the organization (this is the destination, indicated by IP addresses on the right).There are two internet breakouts for reaching the destination and it is seen that only one of them is used most of the time where the latency is high.We saw the same issue using Catchpoint’s Custom Dashboard pinpointing where the latency is higher.Image6: Catchpoint Custom DashboardEnterprise network architecture varies across locations, Catchpoint’s Enterprise nodes can be leveraged to determine whether employee experience problems are internal or external through first-mile testing or testing from within the network or behind the firewall. Monitoring using Enterprise nodes will help gain a complete view of the organization’s network health including reachability, availability, performance, and reliability ensuring that the employee experience is not compromised.Most businesses rely on