November 2021 Summaries

AWS Fargate is a serverless compute engine that enables the deployment of containerized applications without managing underlying virtual machines. It supports Windows containers and can be used in conjunction with Amazon ECS for seamless integration. Datadog, partnering with AWS, offers comprehensive visibility into the health and performance of applications deployed using Fargate and ECS. Users can collect metrics, traces, and logs from their containerized Windows apps to monitor infrastructure, application performance, and troubleshoot issues effectively. This support extends to all types of Windows applications, regardless of deployment method.

Thomas Sobolik is proud to partner with AWS for the launch of support for AWS Fargate on Windows containers, allowing users to deploy containerized applications without managing underlying virtual machines and reducing operational overhead. With Datadog's native support for AWS Fargate on Windows, users can collect performance metrics, traces, and logs from their containerized Windows apps deployed using Fargate and ECS, providing comprehensive visibility into application health and performance. By enabling Datadog's AWS integration, users can start ingesting Fargate metrics from Amazon CloudWatch in just a few clicks, viewing key resource and network metrics at a glance, and deploying the containerized Datadog Agent to their ECS tasks for more granular data. Additionally, users can use Datadog APM to monitor the health and performance of their applications, instrumenting their apps with out-of-the-box tracing libraries and collecting distributed request traces to surface errors and latency. With Datadog's native support, users can now easily leverage infrastructure monitoring, APM, and more to monitor the health and performance of all their containerized Windows apps, regardless of deployment method.

AWS Fargate is a serverless compute engine that allows users to deploy containerized applications without managing compute resources. Datadog has partnered with Amazon to support workloads running on Graviton2, Amazon's ARM64 processor. Through the partnership, Datadog enables monitoring of health and performance metrics for Graviton2-powered serverless workloads alongside other environments. The integration automatically ingests Fargate metrics from Graviton2 deployments via Amazon CloudWatch and provides an out-of-the-box AWS Fargate dashboard. Additionally, the Datadog Agent natively supports ARM-based architectures, allowing users to get more granular metrics by deploying the containerized Agent image in their ECS tasks. The Container Map and Live Containers view provide real-time visibility into containers' performance, enabling users to identify and investigate issues such as CPU or memory overconsumption, code errors, or network latency.

Datadog is proud to be a launch partner with Amazon for their support of AWS Fargate workloads running on Graviton2, a proprietary ARM64 processor. Datadog's AWS integration automatically ingests Fargate metrics from your Graviton2-powered deployments via Amazon CloudWatch, providing a quick birds-eye view of deployment health through the out-of-the-box AWS Fargate dashboard. The Datadog Agent can be deployed to ECS tasks to get more granular metrics, and once deployed, it enables users to observe their workloads' performance at a high level using the Container Map and Live Containers view. These views allow users to group, filter, and inspect containers using tags, providing real-time visibility into container resource metrics, active processes, logs, traces, and network metrics. With Datadog's native support for Fargate on Graviton2, users can now easily monitor the health and performance of their containerized applications, leveraging visualizations, alerts, and more to identify issues and investigate problems in real-time.

Datadog's Session Replay feature enables businesses to analyze user behavior on their websites while ensuring sensitive data is protected through configurable privacy settings. These settings include three obfuscation options - allow, mask-user-input, and mask - which can be configured on a per-page basis. By default, all user inputs are masked using the mask-user-input setting, but this can be modified to obscure more or fewer elements as needed. Privacy settings are inheritable, allowing for granular control over data obfuscation. The three options cater to different use cases: "mask" obfuscates all text and input fields; "mask-user-input" only hides user inputs in form fields; and "allow" keeps all text visible on pages without sensitive data or requiring logins. Session Replay privacy settings help businesses maintain security and compliance while gaining insights into user behavior.

Datadog's Session Replay provides granular control over what data is viewable during a session replay, allowing users to configure different privacy settings based on the context of a session replay and use case. The tool includes three obfuscation options: `mask`, `mask-user-input`, and `allow`, which can be configured on a per-page basis to determine how much detail to obfuscate in a replay. By default, Session Replay automatically masks all user inputs using the `mask-user-input` setting, but users can modify this setting through JavaScript RUM configuration or HTML attributes and classes. The `mask` option fully obscures text and input fields, while the `allow` option keeps all text and input fields visible, making it suitable for public-facing websites with minimal sensitive data. With Session Replay's privacy settings, users can review and analyze user behavior while keeping their data protected and meet security and compliance regulations.

Confluent Cloud and Datadog have integrated to provide a seamless connection between the two cloud-native, managed services. This integration enables users to monitor key metrics from their clusters within minutes without any additional configuration required. The integration also allows for visualizing and alerting on important metrics in real-time. Users can now create a service account, add an API key, and specify which Confluent Cloud resources they want to monitor through Datadog's dashboard. This collaboration between Confluent and Datadog aims to provide full visibility into the users' Confluent Cloud environment and improve overall data management efficiency.

Datadog Synthetic private locations are crucial points of presence for running synthetic tests on internal services within an organization's network. They can be deployed using various orchestrators and work in tandem with the testing tunnel to ensure comprehensive testing coverage. Datadog now offers full visibility into the health and performance of these private locations through Private Location Monitoring and the Datadog Agent, enabling SRE and platform teams to monitor metrics, alerts, container runtime statistics, and service level objectives (SLOs). This helps maintain efficient test infrastructure and allows organizations to deploy reliable applications for their employees and customers.

Confluent Cloud and Datadog have integrated to provide a fully managed, cloud-hosted streaming data service that enables users to get deep visibility into their Confluent Cloud environment with just a few clicks. The integration allows users to visualize and alert on key metrics for their clusters without any further configuration required. To set up the integration, users need to create a Confluent Cloud service account and add an API key, which is then added to the Confluent Cloud integration tile in Datadog. Within minutes of configuring the integration, Datadog begins pulling in metrics from Confluent Cloud resources, including backfilling the previous four hours of data. The integration provides a range of visualizations and alerts for monitoring cluster health and performance, as well as tracking important information like active connections and data consumption ratios.

Datadog Synthetic private locations play a crucial role in organizations' test infrastructure by providing customizable points of presence for running synthetic tests on internal services. These locations can be deployed using various orchestrators and enable seamless scaling with the rest of the fleet. Datadog now offers full visibility into their health and performance through Private Location Monitoring, which includes out-of-the-box metrics and alerts to monitor private locations and host runtime statistics. This allows teams to ensure that private locations are live, up-to-date, and have enough resources to support running synthetic tests. Additionally, Datadog provides deeper insights into host and container runtime performance when deployed alongside private locations, enabling teams to capture runtime metrics, logs, network activity, and more. With Private Location Monitoring, teams can track the performance of all deployed private locations and set objectives for improving their reliability over time with highly customizable SLOs, ultimately building reliable test infrastructure that enables the deployment of performant applications.

React.js is a popular solution for building dynamic and interactive web app frontends since its release in 2013. However, single-page React apps can introduce potential monitoring challenges such as difficulty gleaning insights from large volumes of route change data or collecting context around errors. To navigate these challenges, Datadog's Real User Monitoring team has built a set of React components that can be adapted and integrated into applications to provide more granular data and better insights. These include the React Router Tracker component for cleaner user page view data, the Error Boundary component for better context around errors, and the Context Provider component for appending stacktraces and custom attributes to recorded user actions.

Datadog's Real User Monitoring (RUM) team has developed React components that can be integrated into applications to collect more granular data and better insights from instrumented React apps. These components help navigate the challenges of single-page React applications, including monitoring route changes, getting context around errors, and tracking user actions. The components include the React Router Tracker, Error Boundary, Context Provider, and RumComponentContextProvider, which can be used to collect cleaner user page view data, better error context, append stacktraces and custom attributes to recorded user actions, and add more context to user actions, respectively. These components can be integrated into existing applications with minimal changes, providing a comprehensive solution for tracking user behavior and debugging issues in React applications.

Datadog has introduced Mobile Vitals, a feature that monitors and tracks fundamental health and performance indicators from mobile applications. This includes metrics such as slow renders, JavaScript slow renders, Flutter build and raster time, frozen frames, application not responding, crash-free sessions, CPU utilization, and memory utilization. By tracking these metrics, developers can gain comprehensive visibility into their mobile app's performance, identify areas of improvement, and ensure a smooth user experience. Mobile Vitals provides insights into factors such as battery consumption, crashes, and ANRs, allowing developers to optimize their applications for better performance and user satisfaction.

Datadog Distributed Tracing allows users to search and analyze live traces over a 15-minute rolling window while retaining only the necessary ones through flexible retention rules. Users can now generate metrics from any span using any tag, enabling long-term trend tracking in application performance. This feature helps build meaningful business span-based metrics by leveraging Trace Search and Analytics to query and aggregate spans across various dimensions. The generated span-based metrics are stored at full granularity for 15 months, allowing historical analysis on spans. These metrics can be correlated with other telemetry data, used to configure alerts, or treated as service level indicators (SLIs) for establishing service level objectives (SLOs). This approach helps minimize costs associated with retaining and managing large volumes of spans while ensuring accurate insights into application performance.

Datadog's Distributed Tracing allows users to search and analyze their ingested traces live over a 15-minute rolling window, retain only the ones they need with flexible retention rules, and generate metrics from any span using any tag. This enables tracking of long-term trends in application performance and accurate measurement of system state. With Datadog's metric-based functionality, users can create span-based metrics to monitor their application's performance, graph these metrics on dashboards, configure alerts for potential issues, treat these metrics as service level indicators (SLIs) for establishing service level objectives (SLOs), and derive insights from all of their spans in a cost-effective way.

Datadog Synthetic Monitoring has expanded its API test suite to include UDP and WebSocket protocols, allowing users to proactively monitor real-time applications such as customer support chat platforms and video streaming services for availability and performance issues. The new tests enable the simulation of messages sent to UDP server ports and opening WebSocket connections, with results available from globally distributed managed locations or private internal networks. Datadog Synthetic CI/CD Testing allows users to execute these tests directly within their CI/CD pipelines for early detection and troubleshooting.

Datadog Synthetic Monitoring allows developers to proactively test their applications for availability and performance issues before they impact end-users. The platform supports API tests, including simulated HTTP requests, SSL certificate validation, DNS resolution checks, TCP connection testing, and endpoint pings. Recently, Datadog has added support for UDP and WebSocket API tests to ensure the low-latency requirements of real-time applications such as customer support chat platforms and video streaming services are met. These new tests utilize the UDP transport layer protocol and the WebSocket application layer protocol to facilitate fast data transmission and bidirectional client-server communication. With these tests, developers can detect server connectivity issues, verify network timings, and identify potential problems with their latency-critical applications before they impact end-users. The platform also allows users to configure tests to run on demand or periodically from globally distributed managed locations, as well as private locations within their internal networks.

Azure Government is a dedicated cloud platform designed for public sector organizations with highly regulated environments. Datadog has partnered with Azure to enable users to collect low-latency metrics from all workloads and Azure services hosted on Azure Government. The integration allows for complete visibility into the status and health of infrastructure resources, including Azure Load Balancer and Azure Functions. With over 650 integrations, Datadog enables users to detect issues in workload performance at each layer of their stack and drill down to specific resources to determine root causes. Additionally, Datadog collects data from Azure services to automatically generate additional metrics, providing an exhaustive view of the entire Azure infrastructure. This integration also helps with better inventory management by making it easy to tag resources with metadata for monitoring activities and ensuring compliance policies are followed.

Datadog has partnered with Azure to provide low-latency metrics collection from all workloads and services hosted on Azure Government. This enables public sector organizations to maintain visibility into the status and health of their infrastructure, allowing them to monitor critical services such as Azure Load Balancer and Azure Functions. With full-stack monitoring capabilities, Datadog provides a unified view of the entire Azure ecosystem, including any services running in an Azure Government environment. Additionally, it offers better inventory management by providing metadata tags for Azure resources, making it easier to track outdated or misconfigured infrastructure and identify compliance risks.

Datadog has integrated Ozcode's live debugging solution to enhance visibility into production environments, aiming to help developers identify and resolve issues faster. Ozcode's Live Debugger enables teams to step through code execution flow, view contextual data, and troubleshoot without redeploying applications. This collaboration aims to improve software delivery, reduce mean time to resolution, and enhance end-user experience. The Ozcode team brings valuable expertise and a shared focus on customer satisfaction to Datadog's vision of incorporating observability into debugging workflows.

Renaud Boutet from Datadog is excited to announce that they have welcomed Ozcode, a live debugging solution, to their platform. The two teams share the goal of incorporating observability into debugging workflows and are working together to deliver the most robust experience to customers. Ozcode's Live Debugger provides features such as step-through code execution, contextual data viewing, and dynamic log generation, which help teams identify root causes quickly and reduce mean time to resolution. By combining their tools, Datadog and Ozcode aim to break down silos and improve end-user experience.

The Sensitive Data Scanner is a tool designed to help governance, risk management, and compliance (GRC) teams discover, classify, and protect sensitive information within distributed applications. It provides real-time visibility and control over the data that application services are logging, enabling automatic identification of sensitive data, classification as high or low risk, and protection through scrubbing or hashing for correlation or auditing purposes. The scanner helps eliminate data exposure blind spots and allows organizations to remain compliant with various regulations while keeping data safe. Customizable scanners can detect potential data leaks, and teams can leverage tags to grant or deny access to logs containing sensitive information. Additionally, the Sensitive Data Scanner offers options for automatic obfuscation of sensitive information in logs through hashing or scrubbing. GRC teams can create alerts and visualize flagged logs to monitor services violating compliance standards and take appropriate actions to ensure customer data confidentiality.

The Sensitive Data Scanner is a tool designed to help Governance, Risk Management, and Compliance (GRC) teams discover, classify, and protect sensitive information in distributed applications. It provides real-time visibility and control over logging data, enabling teams to automatically identify and classify sensitive data as high or low risk via searchable tags. The scanner can also protect sensitive data by scrubbing or hashing it for correlation or auditing purposes. Additionally, the Sensitive Data Scanner allows GRC teams to monitor flagged services with alerts and dashboards, visualize flagged logs, and restrict access to restricted services. By using the Sensitive Data Scanner, organizations can remain compliant and keep their customer data safe from potential breaches.