September 2023 Summaries

Boomi is a cloud-based integration platform that helps users connect their applications and data sources. However, monitoring and troubleshooting Boomi Atoms can be challenging due to the automatic purging of logs after 30 days and the need for frequent manual correlation of data from various sources. Kitepipe's AtomWatch integration in Datadog Marketplace addresses these issues by providing crucial visibility into Boomi processes and applications connected to it within a single platform, centralizing all logs and alerts from Boomi alongside logs from your runtime cloud provider. This enables users to monitor critical metrics, logs, and events related to their Boomi processes, proactively monitor the health of their Boomi-connected applications, and pinpoint the root causes of performance issues in Boomi. The AtomWatch integration also allows for long-term trend analysis by retaining Boomi logs for extended periods, which can be essential for identifying persistent but elusive issues to optimize performance.

The AtomWatch integration from Kitepipe enables users to monitor Boomi processes and applications using Datadog, providing a single pane of glass for visibility into logs, platform alerts, and critical metrics. This integration provides proactive monitoring, allowing users to identify potential failure points, troubleshoot issues, and optimize performance. The AtomWatch dashboard offers out-of-the-box (OOTB) dashboards and monitors for key metrics, logs, and events, enabling users to analyze data in depth and customize their monitoring experience. By leveraging Datadog's Log Management capabilities, AtomWatch allows users to retain Boomi logs for months or even years, providing granular visibility into long-term health and performance trends. This integration is now available in the Datadog Marketplace, offering a 14-day free trial for new users.

Amazon SageMaker is a fully managed service designed to simplify the processes of building, training, and deploying machine learning (ML) models, making it suitable for various applications such as recommendation systems, chatbots, and predictive analytics models. The performance and resource utilization of SageMaker ML inference endpoints, as well as the jobs that include training, processing, and batch inferences, are critical for delivering accurate and efficient user experiences. Datadog offers a solution to monitor these metrics by collecting data on latency, errors, resource utilization, and invocations, which are visualized on an integrated dashboard. This monitoring allows for quick identification of issues and optimization opportunities, such as adjusting compute instance types or scaling resources. Additionally, Datadog’s SageMaker integration supports over 850 other integrations, enabling comprehensive monitoring of SageMaker metrics alongside other AWS services, to ensure optimal resource usage and model performance.

Datadog is deeply committed to open-source software, with its Agent, tracers, SDKs, and libraries being open-source from the start. The company embraces an "upstream-first" approach when using open source projects, actively contributing bug fixes and improvements back to the original developers. Datadog has also initiated several open-source projects that are maintained with community support. To further promote open collaboration, the company has launched the Datadog Open Source Hub, a centralized space where users can learn about the projects they maintain and contribute to, as well as explore other related initiatives. The Hub will showcase selected open source projects supported by Datadog observability and offer free accounts for some of these projects.

The Datadog company has been deeply involved with open source software since its inception, producing and contributing to various projects. They have an "upstream-first" approach, working closely with original developers to bring about changes and improvements to open source projects that support their infrastructure. To further facilitate collaboration and community involvement, Datadog is announcing the launch of the Datadog Open Source Hub, a central space for learning about maintained projects, participating in ecosystems, and discovering related initiatives. The hub will showcase open source projects started at Datadog, provide opportunities for contributors to learn from maintainers and other contributors, and highlight projects that have been using Datadog's observability tools to their advantage.

Sofy is a SaaS platform that enables automated testing of mobile apps without writing any code. It tests apps on real iOS and Android devices, allowing developers to optimize performance and debug end-user experiences without setting up or maintaining their own test infrastructure. The Datadog Sofy integration provides deeper insights into app test results by visualizing key metrics such as CPU, memory, network utilization, and user-display load time. This helps identify issues affecting mobile apps and configure alerts to detect performance regressions or bugs causing application crashes. Automated alerts can be used to create incidents in Datadog Incident Management for immediate response and resolution tracking. The integration is available through the Datadog Marketplace, with a free 14-day trial offered for new users.

The Sofy platform is a SaaS solution that enables developers to create and run automated tests on their mobile apps without writing any code, reducing the need for manual testing. Sofy integrates with Datadog, allowing users to visualize performance metrics from their mobile app tests, track device resource utilization, and debug performance issues faster. The integration provides real-time insights into mobile app performance, enabling developers to optimize their applications and ensure a positive end-user experience. By using Sofy and Datadog together, developers can proactively identify and resolve performance regressions or bugs that may be causing application crashes or issues.

Ansible is a configuration management tool that automates deployment, management, and configuration of software on hosts. The Datadog collection, available in both Ansible Galaxy and Ansible Automation Hub, enhances automation workflows with features such as the Datadog Ansible role for installing and configuring the Datadog Agent across infrastructure. Ansible works by managing inventories of hosts and playbooks containing tasks to deploy and configure these hosts. Users can leverage existing open-source content or certified collections like Datadog's, which is Red Hat certified and supported by both Red Hat and Datadog. To install the Datadog Agent using the collection, users need an up-to-date version of Ansible on their control node, followed by installation of the datadog.dd collection via Automation Hub or Ansible Galaxy CLI script. After creating an inventory file specifying hosts to monitor with Datadog, a playbook is created using the imported Datadog role and run against the inventory.

Ansible is a configuration management tool that automates the deployment, management, and configuration of software on hosts. The Datadog collection is now available in both Ansible Galaxy and Ansible Automation Hub, providing access to new automation content such as modules, plug-ins, and roles. Installing the collection enables users to automatically deploy the Datadog Agent and its integrations across their infrastructure. The collection is Red-Hat certified, ensuring that all released updates are ready for use in production environments and fully supported by both Red Hat and Datadog. To get started, users need to install the collection, create an inventory of hosts to monitor with Datadog, and create a playbook that installs the Agent on their inventory of hosts. Once installed, users can easily configure their playbook to install Datadog integrations and gain full visibility across their environment.

The integration of Database Monitoring (DBM) and Application Performance Monitoring (APM) allows for seamless linkage of telemetry data from both sources, enhancing visibility and streamlining troubleshooting processes. By using DBM and APM together, application developers can gain insights into database performance without direct access to the database, while Database Administrators (DBAs) can understand how request traffic from specific services affects the database load. This integration enables developers to collaborate more effectively with DBAs in identifying and mitigating issues related to database performance.

You can seamlessly link telemetry data from Database Monitoring (DBM) and Application Performance Monitoring (APM) to increase visibility and streamline troubleshooting. This allows application developers to easily gain key insight into database performance without needing direct access, while DBAs can understand how request traffic from specific services affects the database load and which services might experience latency due to database performance issues. By connecting these tools, you can correlate service performance with downstream database infrastructure, view APM data to gain context around database performance, and link DBM and APM telemetry for fast, intuitive troubleshooting.

Datadog introduces Cloud SIEM Content Packs, a centralized hub for accessing integration content in its cloud security monitoring solution. The new feature streamlines the process of configuring log sources for Datadog Cloud SIEM, enabling users to monitor their environment for security issues more quickly. Users can explore and activate nine initial Content Packs grouped into four categories: Cloud Audit, Authentication, Collaboration, and Network. Each Content Pack provides valuable insights into security logs, including threat detection, interactive dashboards, investigator widgets, workflow automation, and related content resources. This helps users respond promptly to emerging threats and start generating ROI from their SIEM more quickly.

Datadog Cloud SIEM is a security solution that helps customers protect their cloud environment and SaaS applications against threats with built-in threat detection rules, interactive dashboards, workflow blueprints, and in-depth support resources. To streamline the process of configuring log sources for Datadog Cloud SIEM, Datadog has introduced Content Packs, a centralized hub for accessing integration content. Users can explore and activate Content Packs, which provide valuable insights into security posture, threat detection rules, interactive dashboards, workflow automation, and in-depth support resources. With Content Packs, users can quickly send logs to Datadog Cloud SIEM, access key insights into threats, log activity, and other metrics, and generate ROI from their SIEM more quickly.

The Temporal Platform is an open source programming model that enables users to write and run scalable and reliable cloud applications. It consists of a Temporal Cluster and Worker Processes, which together create a runtime for reentrant processes called Workflow Executions. Temporal's workflows are resilient programs that execute tasks and react to external events, including timers and signals. Datadog has integrated with the Temporal Server, allowing users to visualize the health of their Temporal Server and configure recommended monitors for insightful alert notifications. The integration includes an out-of-the-box (OOTB) dashboard that displays metrics and logs from all components of the Temporal Server, providing visibility into exceptions and warnings. Additionally, four preconfigured monitors send alerts when performance issues are detected in frontend, matching, history, or persistence latency. This integration helps users quickly identify and resolve issues, maintain optimal Worker performance, and enhance their security strategy.

Datadog has introduced Cloud SIEM Investigator for Azure, which provides visibility into Microsoft Azure environments for security teams and DevOps professionals. The tool uses Azure audit logs to visualize user activity across resources such as Azure Storage and Virtual Machines (VMs), enabling users to associate this activity with Azure entities like service principals and end users. This helps in quickly understanding who is doing what within the environment, improving cross-team collaboration on investigations into cloud resources. The integration of Cloud SIEM Investigator with Datadog Log Explorer and Security Signals provides a centralized set of resources for teams to collaborate on identifying potential security threats.

Datadog has announced the release of its Cloud SIEM Investigator for Azure, which provides crucial visibility into Microsoft Azure environments for security teams and DevOps. This tool uses Azure audit logs to help teams visualize activity across resources, including Azure Storage and Virtual Machines, and associate this activity with entities such as service principals and end users. The tool enables teams to visualize Azure activity, drill down into specific entities, improve cross-team collaboration on investigations, and gain enhanced visibility into their Azure environment. It integrates with Datadog's Log Explorer and Security Signals, providing a centralized set of resources for teams to collaborate on identifying potential security incidents and root causes. With this release, organizations can now quickly identify details about the end users and service principals interacting with key Azure resources, enabling DevOps and security teams to align and dig deeper into root causes more efficiently.

The Temporal Platform is an open source programming model that enables users to write and run scalable and reliable cloud applications. It consists of a Temporal Cluster and Worker Processes, which together create a runtime for reentrant processes called Workflow Executions. The platform's workflows are resilient programs that execute tasks and react to external events, including timers and signals. A new integration with the Temporal Server is available, allowing users to visualize the health of their environment, configure recommended monitors to receive insightful alert notifications, and gain efficiency in their workflow management. The integration provides a centralized location for viewing metrics and logs from all components of the Temporal Server, enabling users to quickly identify performance issues and resolve them before they impact production. With this integration, users can also monitor the performance of individual services within the Temporal Cluster and receive alerts when performance issues are detected.

Datadog Click Maps provide visual representations of user behavior on individual pages within an application. By aggregating data from all user sessions and color-coding the most popular actions, Click Maps enable developers to quickly identify patterns in user engagement and troubleshoot issues. The tool also helps create optimal page layouts by strategically placing content for maximum interaction. Additionally, Click Maps provide insights into broader behavioral trends, making it easier to design relevant conversion funnels. By analyzing popular actions on the Insights panel, developers can identify common user journeys and create effective conversion funnels that evaluate pain points in their app. Datadog Heatmaps also offer a Top Elements view that ranks each element based on user interaction, allowing for better-informed decisions about UX optimization.

Google Kubernetes Engine (GKE) is a managed Kubernetes service on Google Cloud Platform (GCP). It comes in two modes: Standard and Autopilot. GKE allows cluster operators to focus on running containerized applications without the overhead of managing the Kubernetes control plane. Monitoring Kubernetes-based environments requires gathering metrics, logs, distributed traces, and other data from across the cluster. Datadog provides components for monitoring GKE with its Agent and Cluster Agent. Deploying these agents enables deep visibility into GKE clusters, including dashboards, visualizations, and application-level insights.

The text discusses the integration of Synthetic Browser tests with Datadog RUM and Session Replay, highlighting its benefits for application development. This new feature allows developers to create tests that mirror real user interactions, enhancing the accuracy and reliability of testing processes. By making the test creation process accessible to various teams within an organization, it encourages collaboration and reduces reliance on a single team or individual. Users can generate tests with a single click, customizing them as needed, which eliminates the need for extensive UI knowledge and helps detect potential issues. The integration with Datadog's platform ensures comprehensive test coverage and supports the efficient rollout of new features, ultimately improving application reliability and return on investment.

Instabug has partnered with Datadog to offer an out-of-the-box (OOTB) dashboard widget along with a software license in the Datadog Marketplace. The OOTB Application Performance Index (Apdex) dashboard widget helps assess user satisfaction and leverage built-in bug reports with Datadog Application Performance Management (APM) and Infrastructure Monitoring to instantly correlate degraded performance with recently identified code issues. An Apdex score is an industry-wide standard that quantifies mobile usability by using various performance and stability metrics to calculate the perceived quality of your app, returning a figure from 0 to 1 that represents the proportion of satisfactory user sessions. The widget translates your performance thresholds into four easy-to-parse buckets—Satisfying, Tolerable, Frustrating, and Crashing—to help you interpret what raw Apdex scores mean for your UX.

Instabug is a tool that helps mobile app developers understand how users experience their apps, providing crucial performance metrics alongside bug reports. This enables developers to proactively handle smaller issues and ensure a smooth user experience (UX). The Instabug Apdex score measures mobile usability by using various performance and stability metrics, returning a figure from 0 to 1 that represents the proportion of satisfactory user sessions. The widget translates this score into four easy-to-parse buckets—Satisfying, Tolerable, Frustrating, and Crashing—to help interpret what raw Apdex scores mean for UX. By combining UX data with crash reports, Instabug provides meaningful, context-driven troubleshooting, allowing developers to tailor their app's performance according to its needs. The tool integrates seamlessly with Datadog Application Performance Management (APM) and Infrastructure Monitoring, enabling developers to troubleshoot and resolve issues quickly.