UX-oriented Datadog observability platform deployment service

Remote professional services for the implementation of the DataDog solution in the AWS cloud, during the client's office hours from Monday to Friday, covering the following DataDog modules:

• Real User Monitoring (RUM)
• Synthetic API Test
• Synthetic Mobile / Web Browser
• CI Visibility

The project is executed in four phases, as described below:

1.Discovery: The project begins with an exploration phase to evaluate the current use of observability functions in the application environment, tagging strategy, log collection, user flows, alerts and service monitoring. On the other hand, in this stage the details of the application architecture, its components, versions and considerations of the environment are reviewed that will function as the basis for the definition of applicable configuration strategies for sampling monitoring data, configuration of key dashboards for the business, specification of metrics, traces, logs, alerts and the monitoring of incidents according to business priorities and optimization opportunities.

2. Implementation: HPCg will carry out the configuration and implementation of Datadog's Real User Monitoring (RUM) capabilities in the application, spanning Android, iOS and tvOS platforms. This includes the integration of Datadog's native SDKs, the enablement of the collection of user experience metrics, as well as the definition of User Journey for the generation of synthetic tests and the identification of pain points at the user level and that are aligned with the objectives and proposed service level of the monitored application.

3. Observability and Proactive Alerting: In this phase, HPCg will create dashboards, monitors, SLOs, aligned with the business and operational objectives defined during the discovery phase. An alert routing plan will be applied for optimal management of incidents or anomalies detected through monitoring functions.

4. Closure: Final validation of the implementation executed in search of detecting and making optimization adjustments if necessary, in addition to verifying the final coverage of Datadog functions, according to the scope of the application and its scenarios.