October 2020 Summaries

Sleuth is a deployment tracking tool that provides insights into CI/CD workflows by monitoring all team deployment tools from one dashboard. It integrates with various components of the deployment pipeline and automatically alerts users about code shipping, manual approvals, and failures. The recent integration with Datadog allows Sleuth to tie rich metrics from Datadog to different sources of change it monitors, providing greater context around features or releases. Sleuth organizes deployments into projects that collect key data from code sources and staging environments, including metrics and errors. With the Datadog integration, users can visualize key performance metrics across all Sleuth-integrated services from a single location, enabling them to detect unusual activity within their deployments and understand how it might be affecting their workflow.

Sleuth is a deployment tracking tool that provides a single dashboard view of team's deployment tools and development processes, integrating with various tools across the code deployment toolkit. Sleuth can automatically alert users to deployment changes, manual approvals, and failures, giving them a deeper level of insight into their CI/CD workflows. The new Datadog integration allows Sleuth users to tie Datadog's rich metrics to the sources of change that Sleuth monitors, providing greater context around features or releases from concept to production. With this integration, Sleuth can use Datadog metrics to make smart inferences about deployment data and detect unusual activity within deployments. The integration also enables visualization of key performance metrics across all Sleuth-integrated services from a single location, allowing users to analyze SLI data, track metric variances, and gain insight into the impact on their deployments.

Amazon Route 53 is a cloud service that provides DNS and traffic routing for applications. It allows users to define multiple routing policies and configure health checks to ensure high availability of services. Datadog, an integration tool, fully supports Route 53 by pulling in CloudWatch metrics and providing full support for DNS logs. Users can monitor Route 53 health and performance metrics, analyze DNS activity with Route 53 logs, visualize key Route 53 log data, and monitor resolver logs for security threats using Datadog's features.

Datadog provides a comprehensive integration with Amazon Route 53 to collect and analyze DNS metrics and logs. This allows users to track the health, performance, and security of their network activity in real-time. Datadog ingests CloudWatch metrics from AWS services, including data on DNS queries, health checks, and resolver endpoints. The platform also supports full integration with Route 53 DNS logs, enabling users to ingest, analyze, and alert on every request to their applications. This provides visibility into the health, performance, and security of backend services. Additionally, Datadog offers out-of-the-box rules for detecting potential security threats in real-time, allowing users to monitor resolver logs for suspicious activity and receive alerts when necessary.

Serverless architectures eliminate the need to manage infrastructure components like servers and containers, allowing developers to focus on writing and deploying code. However, this approach introduces new challenges in monitoring and observability. Datadog's serverless monitoring includes Deployment Tracking, which enables users to correlate serverless code, configuration, and deployment changes with metrics, traces, and logs from their functions for real-time insights into how these changes may affect application health and performance. Deployment Tracking provides a summary of serverless function activity within a specified timeframe, detects stack drift through configuration events, and integrates with various serverless tools to provide a comprehensive view of the serverless deployment lifecycle.

Datadog's Serverless Deployment Tracking provides real-time insight into how changes to serverless code and configuration affect application performance, allowing teams to quickly identify and troubleshoot issues. By correlating metrics, traces, and logs with deployment events, teams can iterate faster and deploy more frequently without sacrificing visibility into their applications' health and performance. Datadog's integration with serverless application bootstrapping tools and deployment management services provides a full view of the serverless ecosystem from a single pane of glass, enabling teams to monitor every step of the deployment lifecycle and detect stack drift with configuration events.

Datadog has a long-standing commitment to open standards, enabling customers to collect data using tools and libraries that fit best into their workflows. The company's source code for the Datadog Agent is open source and available on GitHub, and it is proud of its ongoing partnership with the OpenTelemetry project. AWS Distro for OpenTelemetry extends the upstream CNCF OpenTelemetry project by collecting metadata from AWS resources, as well as trace data from AWS SDK and AWS X-Ray. This distribution has been carefully optimized, secured, and tested by AWS to ensure that it doesn't degrade the performance or stability of systems. Customers can easily configure AWS Distro for OpenTelemetry to send metrics and traces to Datadog by adding a `datadog` exporter to their OpenTelemetry configuration YAML file along with their Datadog API key. The partnership between Datadog and AWS aims to improve monitoring workflows, regardless of architectures, and break down barriers to data portability.

Datadog Distributed Tracing is a solution that helps organizations manage tracing costs and gain real-time visibility into application performance. It offers fine-grained ingestion controls, tag-based retention filters, live search, and live analytics to troubleshoot issues in distributed systems, serverless computing, and containerized environments. With Datadog Distributed Tracing, teams can control the cost of tracing by adjusting the trace volume and sampling rate per service based on their criteria. The solution also enables real-time investigation of every ingested span by any tag over a rolling 15-minute window, allowing for quick identification of issues such as application outages or unresponsive services. Additionally, Datadog Distributed Tracing provides long-term analysis capabilities through retention filters, which can capture critical performance telemetry and business context for up to 15 days. The solution helps teams pinpoint the source of customer-reported issues with live search, determine how widespread an issue is with live analytics, retain only the traces that are important to their business, and enjoy flexible and controllable tracing.

Datadog's Reference Tables enable users to enrich logs with business-critical data, providing more contextual information for resolving application performance issues. To create a new table, users can upload a CSV file and select the primary key column, which is then used by a Lookup Processor to add additional data as attributes to incoming logs. This allows users to conduct security investigations on historical logs, organize logs by logical units, map error codes to descriptive error messages, and more. Reference Tables can be linked to cloud buckets for automatic updates, ensuring that the latest data is always available.

Terraform is an infrastructure-as-code tool that helps teams manage cloud environments across multiple service providers. It enables quick provisioning of compute instances and similar resources from infrastructure providers, as well as managing platform-as-a-service and software-as-a-service resources. The Datadog provider allows users to build Terraform configurations to manage dashboards, monitors, cloud integrations, Synthetic browser or API tests, and more within their Datadog environment. With the provider, you can implement monitoring as code, which enables you to instantly set up monitoring for your containers, clusters, instances, and more as you create them.

Datadog Incident Management is an integrated incident management tool designed to improve the speed and efficiency of incident response within organizations. It offers a range of features, including customizable workflows, automated notifications, collaboration tools, and analytics capabilities, that enable teams to quickly identify and resolve issues. The tool integrates with multiple platforms and allows users to declare incidents from various locations across the Datadog platform, as well as initiate responses directly from Slack. With its focus on accessibility, integration, and automation, Datadog Incident Management aims to provide a structured incident response plan that can be tailored to meet the specific needs of each organization. By leveraging customizable settings, automation, and analytics capabilities, users can optimize their incident management processes and derive valuable insights from post-incident reviews.

The Datadog Slack integration enhances organizational communication by allowing seamless access to Datadog's monitoring resources within Slack, minimizing context switching for DevOps and security teams. It facilitates incident management by enabling users to declare and manage incidents directly within Slack channels through commands like "/datadog incident," creating dedicated channels for coordinated troubleshooting. Additionally, the integration provides features such as incident timeline tracking, manual message addition, and Slack reactions for efficient incident documentation. The integration's Home tab offers quick access to key monitoring resources, including dashboards and notebooks, while automatic link unfurling and the "/datadog dashboard" command simplify sharing visual data within Slack. The Bits AI copilot, available in preview, provides concise incident summaries to keep responders updated with minimal effort. The integration is accessible via the Slack App Directory or Datadog's interface, with a free trial available for new users.

Last year, Datadog started building a Continuous Profiler product, with the first languages being Java and Python. Java has profiling tools built into its Virtual Machine, while Python does not have comparable tools. Profiling is a form of dynamic program analysis that measures software resource usage, such as CPU time or memory allocation. It differs from tracing, which provides detailed timelines of application execution but offers little insight into code-level behavior and performance in the operating system. Datadog's Python profiler uses statistical profiling to observe a program's activity intermittently, offering a trustworthy representation of resource consumption with low overhead. The profiler consists of components such as recorders, collectors, exporters, and schedulers, designed for simplicity and compatibility across environments.

The Datadog team has launched a Continuous Profiler product that targets Java and Python languages. The profiler is designed to be always-on and provides insight into application behavior, unlike traditional profiling tools like `cProfile` in Python. Statistical profiling is used instead of deterministic profiling due to the latter's high overhead. The Datadog Python Profiler is built with a focus on low overhead and simplicity, using components such as collectors, exporters, and a scheduler. It supports various data collectors, including a stack collector that measures CPU time and memory allocations, and exports data in the pprof format. The profiler aims to provide a great experience for continuous profiling in Python, making it an exciting development in the field of software performance analysis.

Continuous integration and continuous delivery (CI/CD) pipelines have become essential in modern software development. However, they also introduce new challenges such as bad code deploys leading to downtime and loss of revenue. Datadog's Deployment Tracking feature helps teams overcome these issues by using a unified tag that aggregates performance data based on the code version's infrastructure assets, traces, trace metrics, profiles, and logs. This enables developers to compare the performance of new code with existing live code, quickly identify problems in services and endpoints, and roll back releases if necessary. Deployment Tracking is compatible with various deployment strategies like canary deployments, blue/green deployments, and shadow deployments. It extends Datadog's APM capabilities and provides visibility into the entire application development lifecycle.

Watchdog has introduced new enhancements that provide more visibility and context around application issues. The latest version automatically groups related APM anomalies across different services into a single story, reducing noise and speeding up troubleshooting. It also adds context to alerts and surfaces anomalies within Kubernetes clusters without any setup required. Additionally, Watchdog now includes correlated metrics and dashboards for deeper investigation of issues. These new features are generally available and can be used by existing Watchdog users with no additional setup needed.

Watchdog has introduced new features that enhance its anomaly detection capabilities, providing more visibility and context around application performance issues. The new enhancements automatically group related APM anomalies into a single story, offering actionable insights for root cause analysis, and add context to alerts. Additionally, Watchdog now surfaces anomalies within Kubernetes clusters without setup, and correlates stories with related dashboards and metrics. These updates aim to reduce noise, speed up troubleshooting, and provide more context for root cause investigation.

Datadog's Deployment Tracking feature helps teams implement continuous integration and delivery pipelines by providing visibility into the performance of code deployments. It uses a unified `version` tag to aggregate data on infrastructure assets, traces, trace metrics, profiles, and logs, enabling developers to compare the performance of new code against existing live code. This allows for quick rollbacks in case of issues, ensuring minimal customer-facing impact. Deployment Tracking is integrated with Datadog's APM features, providing out-of-the-box graphs that visualize RED (requests, errors, and duration) metrics across versions, making it easier to spot problems before they become serious issues.