September 2018 Summaries

The Datadog Summit, scheduled for October 18 in London, aims to celebrate and foster community among engineers, developers, and other tech professionals. Attendees will have the opportunity to learn from experts who have successfully implemented observability cultures within their organizations. Practical advice on improving system performance and reliability using Datadog data and insights will be shared. Additionally, workshops covering infrastructure monitoring, distributed tracing, and log analytics will be conducted. The event is free but requires RSVP due to limited seating.

Datadog Summit will be held on October 18 in London, featuring presentations and workshops covering observability, infrastructure monitoring, distributed tracing, and log analytics. The event aims to celebrate the community of Datadog users and developers who contribute integrations and feedback that help improve the platform. Datadog staff will also showcase the latest product features and provide hands-on training sessions to attendees. The summit is a free event with limited space available, so early registration is recommended.

The integration of log management into Datadog aims to solve problems associated with separate platforms for monitoring service health and managing logs. By unifying all data in one platform, users can access important context without switching tools or manually navigating through different systems. This results in faster identification and resolution of issues. Log analytics graphs can now be added to Datadog dashboards, allowing users to visualize log analytics, metrics, and APM data together for a comprehensive view. The integration also enables monitoring high-cardinality data and graphical representation of numerical values from logs. Template variables allow dynamic modification of the scope of log analytics graphs, enabling seamless pivoting between different views in Datadog. This leads to faster and more efficient investigation processes, allowing teams to better distribute work and improve overall performance.

Datadog has introduced log management to its platform, allowing users to visualize log analytics data in dashboards alongside metrics and APM data. This unifies all data in one platform, eliminating the need to switch between tools and contexts to access relevant information. The new feature enables users to monitor high-cardinality data, graph numerical values from logs, and apply tag-based filters for more seamless correlations and informed conclusions. With log analytics, users can quickly investigate issues, validate responses, and assess customer impact in a single platform, making it easier to distribute the work of investigation across teams and improve incident response times.

Datadog APM is a comprehensive application performance monitoring tool that has added new features like App Analytics, Watchdog, and the Service Map to enhance visibility and performance optimization. It supports multiple programming languages, including Java, Python, Ruby, Go, and Node.js, with upcoming support for .NET and PHP. App Analytics allows users to search and analyze APM data using custom tags, while Watchdog automatically detects anomalies using machine learning algorithms. The Service Map visualizes microservices and their dependencies, aiding in root cause analysis. Datadog APM integrates seamlessly with the three pillars of observability—traces, metrics, and logs—offering end-to-end request tracing and allowing for the creation of customized alerts and dashboards. Companies like Airbnb, Square, and Zendesk utilize these features to monitor application performance effectively.

AWS has expanded its ecosystem to include various services that help manage and automate complex cloud environments. To monitor these services, it's essential to track key metrics that provide insights into performance, usage, and resource utilization. This includes metrics such as CPUUtilization, DiskReadBytes, StatusCheckFailed, RequestCount, SurgeQueueLength, HTTPCode_ELB_5XX, Latency, HealthyHostCount, UnhealthyHostCount, FreeStorageSpace, DatabaseConnections, ReadLatency, WriteLatency, DiskQueueDepth, CurrConnections, SetTypeCmds, GetTypeCmds, CacheHits, CacheMisses, Evictions, SwapUsage, Desired task count vs. running task count per service, MemoryUtilization, CPUUtilization, Node status, Memory utilization, Disk utilization, ProvisionedConcurrencyInvocations, ProvisionedConcurrencyUtilization, Duration, Errors, Invocations, ConcurrentExecutions, Throttles, and Provisions. Monitoring these metrics can help ensure that your AWS services are functioning properly, providing a better understanding of performance, usage, and resource utilization. Additionally, adopting an automated, scalable AWS monitoring strategy will enable you to keep tabs on your infrastructure, even as hosts and services dynamically scale and update in real-time.