November 2023 Summaries

CoTerm is a shared terminal that enables real-time collaboration among engineering teams through near-zero latency. It allows developers to pair program and even mob program in their terminal with up to 10 team members, while also providing built-in audio and video chat and multi-user screen sharing. The key benefits of CoTerm beta include speed, enhanced collaboration, annotations, and security features such as data encryption and secret filtering. CoTerm is a new beta feature in CoScreen, the collaboration tool by Datadog that was built for engineers, by engineers.

CoScreen, a collaboration tool by Datadog, introduces a shared terminal feature that enables real-time engineering teamwork through a single tool. This new beta feature allows up to 10 team members to pair program and mob program in their terminals with near-zero latency, sharing control and editing collaboratively in real time. The collaborative terminal features speed, enhanced collaboration, annotations, security, and other benefits such as sharing the entire display or individual windows, allowing multiple users to share screens simultaneously, and retaining control of any apps without letting others steal the window and mouse focus. CoScreen's collaborative terminal works by using a PTY module, CoScreen, and an underlying open-source video conferencing platform Jitsi, with data channels securely transmitting encrypted PTY data alongside remote control data, annotations, and other metadata. The feature is available as a beta release for free, with users able to sign up for a 14-day trial to try it out.

Amazon has introduced a high-performance storage class called S3 Express One Zone for its Simple Storage Service (S3). This class offers consistent single-digit millisecond data access and is designed for frequently accessed datasets. It replicates and stores data within a single AWS Availability Zone, scales to process millions of requests per minute, and uses hardware and software optimized for low latency. Compared to Amazon S3 Standard, S3 Express One Zone can improve data access speeds by 10 times and reduce request costs by 50 percent. Datadog has partnered with AWS to support the launch of S3 Express One Zone, offering Application Performance Monitoring (APM) that provides detailed insights into file I/O latency and throughput patterns for further application optimization.

The Google Cloud Security Command Center (SCC) helps monitor instances for weak points and potential attacks by bringing together findings from all Google Cloud services. Datadog now collects these findings in Cloud SIEM, allowing users to view alerts from Google alongside issues from the rest of their infrastructure. This integration enables centralized security monitoring for entire cloud environments, quick remediation of issues with enhanced finding context, and visualization of Google Cloud security findings in Datadog.

To monitor Google Cloud instances for weak points and potential attacks, you can access the Google Cloud Security Command Center. This brings together findings from all your Google Cloud services, helping detect misconfigurations and code-level vulnerabilities across your system. Datadog now collects these findings in Cloud SIEM, allowing you to view alerts from Google alongside issues from the rest of your infrastructure. The integration enables centralizing security monitoring for your entire cloud infrastructure, quickly remediating issues with enhanced finding context, and start visualizing Google Cloud security findings in Datadog.

Datadog is proud to partner with Amazon to support the launch of S3 Express One Zone, a high-performance storage class that delivers consistent single-digit millisecond data access for latency-sensitive applications. Datadog Application Performance Monitoring (APM) can help users track S3 Express One Zone requests and performance metrics to optimize their application, verify they're using the right storage class for their use case, and gain visibility into S3 usage and activity across their organization. By leveraging APM, integrations, and dashboards, users can make data-driven decisions and improve their application's performance, while also optimizing their application for S3 Express One Zone and switching to a more suitable storage class when needed.

As government agencies increasingly adopt cloud technologies, they must comply with stringent security standards like FedRAMP for civilian agencies and DISA's guidelines for the Department of Defense. Datadog, a cloud monitoring service, is pursuing higher FedRAMP High authorization, matching the DoD's Impact Level 5, allowing public-sector organizations to utilize its services for monitoring applications and infrastructure. Datadog offers full visibility into cloud environments by integrating with major providers like AWS, GCP, and Azure, enabling agencies to efficiently monitor system health and performance. Features such as setting alerts on key metrics, simulating user behavior, and visualizing server status help maintain operational efficiency and simplify troubleshooting. Datadog supports over 850 technologies, and both existing and new users can access its GovCloud solutions, with new users offered a 14-day free trial.

The Datadog Resource Catalog is designed to help administrators of expanding, highly distributed infrastructures efficiently govern their resources by providing a complete inventory and mapping interrelationships. It enables users to view, sort, filter, and drill into all their resources to find key metadata, ownership info, and service relationships. The Resource Catalog can be used for various purposes such as understanding team ownership of resources, planning upgrades, accessing configuration information, maintaining security posture, improving attribution, and planning migrations. It also provides useful context during incident investigations and helps in spotting security threats and misconfigurations.

The Datadog Resource Catalog is a unified inventory of all resources and their interrelationships, enabling organizations to proactively govern their infrastructure. It provides a Governance view where policies can be defined to help close attribution gaps and accomplish broad platform engineering projects within the organization. The catalog helps with understanding and fixing tagging, driving awareness and compliance for infrastructure best practices, discovering resource-level misconfigurations and threats, optimizing cloud spend with visibility into resource costs, and governing cloud from an encyclopedic view. It offers features such as inventory tab to surface insights about all resources in the environment, governance view for custom policies, security tab for understanding attack surfaces and receiving actionable insights about security misconfigurations and active threats, cost tab for visibility into resource costs, and a powerful way to proactively govern infrastructure.

Datadog's Sensitive Data Scanner helps organizations manage sensitive information in their telemetry data by automatically identifying sensitive data in logs, APM traces, and real user monitoring (RUM) events. It enables users to define high- and low-risk sensitive information using searchable tags and redact or hash that information as needed. The scanner provides a central interface for discovering, triaging, troubleshooting, and tracking data compliance issues. Its close integrations with Case Management and Incident Management help kickstart investigations into potential leaks and enable users to quickly start a coordinated response to any sensitive data issue.

Datadog Security Inbox is a new solution designed to help engineering teams manage security vulnerabilities and misconfigurations across cloud environments. It consolidates insights from Cloud Security Management (CSM) and Application Security Management (ASM), providing a unified view of risks. The tool enables users to filter findings by team, prioritize issues based on severity, and detect attack paths resulting from multiple weak points. By offering actionable insights into critical cloud risks, Datadog Security Inbox aims to improve security posture and save time for security and engineering teams.

The Container Images view in Datadog's Container Monitoring provides key insights into every image used in your environment, helping detect and remediate security and performance problems that can affect multiple containers. It offers a centralized location for assessing the state of container images and their issues, with each image labeled with characteristics such as age, size, source, and number of containers running it. The view is integrated with the rest of Datadog's Container Monitoring, allowing seamless pivoting between features for fast troubleshooting. Additionally, it lists any vulnerabilities detected within your images by CSM Vulnerability Management, helping identify potential security weaknesses and prioritize image deployments with larger footprints.

Datadog APM now supports custom instrumentation for AWS Lambda using the OpenTelemetry Tracing API, allowing developers to capture full end-to-end traces from any W3C-compliant service and gain enhanced visibility into their serverless applications. This feature complements OTel API support in Datadog's APM tracing libraries for non-serverless applications. With complete support for W3C trace header propagation across all runtimes, developers can achieve granular visibility without compromising data portability and interoperability.

Identity and Access Management (IAM) systems are crucial in securing environments but their mismanagement often leads to breaches and insider threats. Datadog has introduced Cloud Infrastructure Entitlement Management (CIEM), a new feature of its Cloud Security Management (CSM). This enables users to proactively identify and remediate identity and access risks in AWS environments, with support for other cloud providers planned. Datadog CIEM uses best practices and research to detect and prioritize IAM-based risks such as administrative privileges, permissions gaps, large blast radius, privilege escalation, and cross-account access. It provides detailed descriptions of issues and suggested remediation steps, along with advanced insights for each identified risk. Datadog CIEM is now generally available for AWS users, with documentation and a 14-day free trial available.

Datadog integrates with AWS Identity and Access Management (IAM) Access Analyzer to provide visibility into the security of IAM resources alongside other AWS resources within a single pane of glass. The integration enables administrators and security teams to identify unused permissions, passwords, IAM users and roles, and access keys. With Datadog's IAM Access Analyzer integration, users can easily discover and review any unused access granted to IAM resources. AWS IAM Access Analyzer uses automated reasoning to analyze resource policies and detect unintended access to supported AWS resources. The integration with Datadog allows users to monitor the security of their infrastructure and keep tabs on the state of their resource policies, getting alerted about critical issues or misconfigurations.

Datadog's Sensitive Data Scanner is a tool designed to help organizations manage sensitive information in their telemetry data, ensuring compliance with governance, risk management, and security requirements. The scanner automatically identifies sensitive data, defines high- and low-risk categories, and provides redaction or hashing options. It offers a centralized interface for comprehensive visibility into potential data compliance issues, context-aware classification, and fast troubleshooting. Sensitive Data Scanner enables users to discover, triage, troubleshoot, and track data compliance issues through a central interface, providing key information such as scanning rules, sensitive data events, and blast radius details. The tool also integrates with Datadog Case Management and Incident Management, allowing for coordinated responses to potential data leaks and enabling the creation of cases, Jira tickets, and remediation tasks. By leveraging Sensitive Data Scanner, organizations can scale their data compliance posture, conduct triage and troubleshooting, and assign and track remediation tasks, ultimately ensuring adherence to strict compliance requirements and regulations.

Datadog Security Inbox is a new solution that aims to equip engineering teams with actionable insights to meaningfully reduce risk in complex cloud environments. It consolidates vulnerabilities, security signals, misconfigurations, and identity risks into a unified view, providing contextualization and prioritization of risks. The platform helps teams identify the top actions they can take to strengthen their security posture across their cloud accounts, clusters, containers, and applications. Security Inbox enables users to discover relevant risk findings, prioritize risks based on severity, correlate risks to detect attack paths, and receive immediate, actionable insights into critical cloud risks.

Datadog APM now supports custom instrumentation for AWS Lambda using the OpenTelemetry Tracing API, enabling detailed insights into performance and security. This enhancement complements existing support for non-serverless applications and provides complete frontend-to-backend traces across any upstream or downstream service instrumented using OTel SDKs, Datadog's tracing libraries, Jaeger, or custom instrumentation that supports W3C trace headers. With complete support for W3C trace header propagation, developers can capture full end-to-end traces from any W3C-compliant service and get enriched visibility into serverless applications using vendor-neutral code. Datadog APM offers the best of both worlds, bringing together portability of custom OTel instrumentation with enriched visibility through features like tag enrichment, Cold Start Tracing, Lambda payload capture, profiling, and Application Security Management (ASM).

Datadog has announced the launch of Cloud Infrastructure Entitlement Management (CIEM), a new feature within its Cloud Security Management (CSM) platform, to help organizations proactively identify and remediate identity and access risks in their AWS environments before they can be exploited by threat actors. Datadog CIEM leverages industry best practices and attack vectors to automatically detect and prioritize identity risks for users, roles, groups, policies, EC2 instances, and Lambda functions, including administrative privileges, permissions gaps, large blast radius, privilege escalation, and cross-account access. The platform provides detailed descriptions of issues and suggested remediation steps, along with advanced insights to understand the scope of each identified risk, enabling organizations to efficiently mitigate identity risks using out-of-the-box Workflow Blueprints and automation features. Datadog CIEM is now generally available for AWS users and offers a 14-day free trial for new sign-ups.

The text discusses the challenges of vulnerability management in the face of rapid technological deployment and diversified resources, emphasizing the difficulty of maintaining security compliance and visibility into exploitable vulnerabilities. Datadog Cloud Security Management (CSM) addresses these challenges by continuously scanning containers, hosts, and serverless functions for vulnerabilities, providing a comprehensive view of security posture. CSM prioritizes exploitable vulnerabilities using runtime observability and offers tools for rapid remediation, including a centralized dashboard, automated operations, and integration capabilities. The platform helps organizations efficiently manage vulnerabilities by recommending targeted fixes and automating remediation processes, ultimately aiming to streamline security management and compliance adherence across diverse environments.

The text discusses the integration of Datadog with AWS CodePipeline to enhance CI Pipeline Visibility, enabling developers to monitor and improve the performance and reliability of their CI/CD systems. This integration allows for proactive monitoring of pipeline health, identification of errors, and creation of alerts to notify relevant teams of failures, which is crucial for maintaining an efficient software development life cycle. By offering a customizable dashboard and detailed metrics, Datadog helps teams to identify performance issues and bottlenecks in their pipelines, thus ensuring a smooth and cost-effective release process. The integration is designed to provide seamless visibility and monitoring capabilities across AWS services, aiming to prevent potential problems from impacting development teams and customers.

Datadog has introduced the App Builder, a low-code interface designed to enhance the efficiency of monitoring and remediation tasks directly within the Datadog platform. This tool allows users to create applications using pre-built UI components, integrate Datadog data sources, and execute over 300 actions across various platforms, including AWS and Azure. The App Builder facilitates the creation and management of apps through features like drag-and-drop components, API connectors, and customizable queries, allowing seamless integration with existing systems and rapid response to issues. Additionally, it offers blueprints for common use cases and supports generative AI functionalities for advanced applications. With these capabilities, organizations can streamline operations, reduce context switching, and enhance their monitoring stack's functionality.

In the evolving landscape of technology deployment and security threats, managing vulnerabilities efficiently has become a complex task for organizations striving to maintain robust security postures and adhere to compliance standards such as SOC 2, PCI, HIPAA, and FedRAMP. Datadog Cloud Security offers a comprehensive solution by providing end-to-end visibility and continuous scanning of containers, hosts, and serverless functions for vulnerabilities across CI/CD pipelines and production environments. By leveraging runtime observability, Datadog helps prioritize and remediate exploitable vulnerabilities, assigning a severity score to guide security teams in addressing the most critical threats swiftly. The platform enables rapid remediation with one-click fixes and supports automation through features like Automation Pipelines, which streamline operations and reporting. Integrating seamlessly with existing workflows, Datadog's Cloud Security empowers infrastructure and security teams to maintain high productivity while effectively managing vulnerabilities, supported by a centralized view and detailed insights into each vulnerability.

Datadog has introduced Cloud Security Identity Risks, an updated Cloud Infrastructure Entitlement Management solution designed to proactively identify and mitigate identity and access risks in AWS environments, with future support for other cloud providers. This tool helps manage the growing complexity of identity and access management (IAM) systems, which are often mismanaged, leading to breaches and insider threats. By leveraging current IAM configurations, resource usage data, and industry best practices, it can automatically detect and prioritize various identity risks, including administrative privileges, permissions gaps, and cross-account access. The solution provides detailed insights and suggested remediation steps for each identified risk, supporting the principle of least privilege by recommending the removal of unused permissions. Datadog also offers Workflow Automation to streamline the remediation process, with pre-configured blueprints for addressing IAM-related risks. Cloud Security Identity Risks is now generally available for AWS users, with resources and documentation provided for onboarding and further exploration.

Amazon Bedrock is a fully managed service that provides foundation models (FMs) from leading AI companies and tools for building generative AI applications. Datadog integrates with Amazon Bedrock, allowing users to monitor their FM usage, API performance, and error rate with runtime metrics and logs. The integration enables monitoring of AI models' API performance and errors, optimizing model usage, and gaining insights into model prompts and responses through logs. By using this integration, organizations can gain deeper visibility into their generative AI products and evaluate their performance.

AWS Step Functions is a service that allows users to coordinate activities from various services such as AWS Lambda, Amazon EKS, and Amazon API Gateway to build serverless workflows. Datadog's State Machine Map provides a visualization of the performance of these workflows by showing each state in the workflow along with its logs, errors, and latency metrics. The map helps users monitor their state machines' performance and troubleshoot issues such as errors, latency, and unexpected behavior. It also allows users to understand the branches in their state machine and drill down into any state's data for further investigation. By leveraging both the State Machine Map and the flame graph, users can quickly detect failed workflows, spot the affected state, and determine a root cause.

AWS Step Functions provides a workflow orchestration service that coordinates activity from hundreds of services to build and manage serverless workflows. To fully understand the performance of your Step Functions workflow, you need to see all its states, their relationship, and data describing their performance. Datadog's State Machine Map offers a high-level visualization of your Step Functions workflow along with execution details from each state, including logs, errors, and latency metrics. The map provides valuable context and actionable data for each Step Functions execution, helping you monitor performance and troubleshoot issues such as branch logic and state machine failures. By visualizing your workflows, understanding branches, and drilling down into monitoring data, you can quickly identify and debug problems in your Step Functions workflow.

Amazon Bedrock is a fully managed service that provides foundation models built by leading AI companies and tools for building generative AI applications. Datadog integrates with Amazon Bedrock, allowing customers to monitor their FM usage, API performance, and error rate with runtime metrics and logs. The integration enables monitoring of invocation latency, input token count, output token count, and other performance metrics, providing insights into model usage and optimization opportunities. Additionally, the integration provides log collection capabilities, enabling qualitative analysis of customer prompts and responses. With Datadog's Amazon Bedrock integration, customers can gain deeper visibility into their models' performance, identify issues, and optimize their AI applications.

Tailscale is a modern remote access solution that enables encrypted point-to-point connections using the open source WireGuard protocol. It allows users to easily scale, segment, and manage their private networks as their business grows. Datadog and Tailscale have announced an integration that helps tailnet administrators visualize log activity and set up alerts for suspicious or sensitive events. The new integration enables users to monitor traffic patterns, quickly spot anomalies, identify potential security threats, and receive useful alerts through preconfigured monitors.

Tailscale is a modern remote access solution that allows customers to easily scale, segment, and manage private networks as their business grows. It enables encrypted point-to-point connections using the open source WireGuard protocol, allowing devices on the network to only communicate with each other. Tailscale users can grow their private networks by adding users and devices as needed or limit access using identity-aware access control list capabilities. The new integration with Datadog allows tailnet administrators to easily visualize log activity, set up alerts for suspicious events, and monitor traffic patterns in their network. With the integration, users can analyze flow logs, identify potential performance issues, and pinpoint suspicious activity for investigation and analysis. The integration also comes with out-of-the-box monitors that generate alerts when detecting potential sensitive changes or anomalous traffic patterns, allowing administrators to quickly respond to security threats and ensure their tailnet is performant and secure.

Fleet Automation is a tool that enables efficient governance and management of observability components such as Datadog Agents at scale. It provides a centralized view into the configuration of all installed Agents, ensuring consistency across an organization's entire fleet. This feature allows teams to quickly identify any gaps in their observability coverage and troubleshoot configuration issues faster by surfacing each Agent's YAML configuration file. Fleet Automation is now available in public beta for users to manage their Datadog Agents more effectively.

Fleet Automation provides a centralized view into the configuration of all Datadog Agents installed on hosts across an organization, allowing teams to efficiently govern and manage observability components at scale. This centralization enables uniform visibility into infrastructure and applications, ensuring that every host is configured correctly for monitoring purposes. Fleet Automation also helps troubleshoot configuration issues faster by surfacing Agent YAML files and enabling the creation of support tickets without logging into affected hosts. Additionally, it provides facets to filter data further, such as viewing Agents based on their version or enabled integrations, and enables security best practices like rotating API keys across a fleet of Agents. By bringing configuration management into a single view, Fleet Automation helps SREs focus on core observability while maintaining efficiency and security.

Buoyant Cloud, a component of Buoyant Enterprise for Linkerd, addresses security, reliability, and observability gaps in Kubernetes environments by securing and monitoring communications between workloads. It proactively alerts users to potential issues, such as unauthorized traffic and necessary updates, enhancing the overall health of Linkerd deployments. Datadog has integrated Buoyant Cloud into its platform, allowing organizations to monitor Linkerd workload traffic, rollout events, and metrics alongside other telemetry data. This integration provides a unified view of system events and metrics, aiding in troubleshooting and security management. Users can visualize real-time and historical metrics, facilitating the identification of performance issues and security threats, such as brute-force attacks, enabling swift action to mitigate risks. The collaboration between Datadog and Buoyant Cloud empowers teams to automate security best practices and improve network performance management, ultimately reducing mean time to resolution (MTTR) and ensuring application performance and security.

The State of Cloud Security study analyzed the security posture of thousands of organizations using AWS, Azure, and Google Cloud. Key findings include long-lived cloud credentials exposing cloud identities, inconsistent enforcement of multi-factor authentication (MFA), insufficient adoption of IMDSv2 in AWS, varying use of public access blocks on storage buckets across platforms, and non-administrator permissions allowing access to sensitive data or privilege escalation. Recommendations include minimizing long-lived cloud credentials, enforcing MFA for cloud users, using IMDSv2 on Amazon EC2 instances, blocking public access proactively on cloud storage services, limiting privileges assigned to cloud workloads, and limiting network exposure of cloud workloads. Datadog Cloud Security Management (CSM) can help organizations improve their security posture by identifying long-lived cloud credentials, tracking down stale cloud credentials, enforcing MFA for cloud users, using IMDSv2 on Amazon EC2 instances, finding overprivileged roles, and finding publicly exposed workloads.

The modern application landscape is evolving rapidly, with new tools and technologies emerging to enable faster deployment of production code. However, this has also led to changes in the risks associated with application security, necessitating an evolution in the security discipline to adapt to new types of attacks. While Dynamic Application Security Testing (DAST) has become a widely used approach for detecting vulnerabilities, many organizations are shifting towards solutions that provide deeper visibility into production code. The challenges of using DASTs include their limited scope and "launch and wait" approach, which can create significant gaps in an organization's overall security strategy. To address these gaps, security teams need a solution that not only improves the scope of vulnerability detection but also their remediation efforts. Interactive Application Security Testing (IAST) is an instrumentation-based approach that analyzes applications in their running state and monitors requests and their interactions with each internal layer. This approach enables IASTs to integrate with existing infrastructure, continuously monitor all production traffic for vulnerabilities, and provide real-time security insights. Datadog Application Vulnerability Management uses the IAST approach to efficiently detect security vulnerabilities in production environments. It offers 100 percent coverage against the OWASP benchmark and provides the necessary context for confirming and fixing vulnerabilities. By leveraging IASTs, organizations can maintain secure, reliable production environments with continuous, code-level vulnerability detection.

The modern application landscape is rapidly evolving, creating new tools and technologies that allow organizations to deploy production code faster. However, this evolution has also led to significant changes in the risks to application security, requiring a shift in the security discipline to adapt to new types of attacks. Traditional solutions like Dynamic Application Security Testing (DAST) have become widely used, but they have limitations, including lack of granular visibility into an application's code and architecture, which can lead to gaps in an organization's overall security strategy. An instrumentation-based approach through Interactive Application Security Testing (IAST) offers a solution by providing continuous monitoring of production traffic for vulnerabilities, real-time security insights, and greater coverage against a wider range of vulnerabilities than traditional solutions. IASTs like Datadog Code Security enable teams to detect deep-level vulnerabilities that affect an application's code or architecture, provide context for finding the source of a detected vulnerability, and offer 100 percent coverage against the OWASP benchmark, ensuring complete accuracy in detecting vulnerabilities.

The text discusses the integration of Datadog with Azure Key Vault to help users monitor the expiration of credentials stored within the Vault, such as SSH keys, API keys, and database passwords. This integration generates automatic alerts when a credential's expiration date approaches, allowing users to renew them in advance and avoid application downtime. It provides an example of how an engineer might use this feature to prevent losing access to essential environments during testing. The document also explains how Datadog handles certificate and key expiration as a single event and describes how it manages missing permission events, which notify users when Datadog lacks access to certain Key Vaults. Overall, the feature aims to ensure uninterrupted access to critical Azure resources by proactively managing credential expirations and permissions, with guidance on setting up the necessary permissions for Azure App Registrations.

As cybersecurity threats like Distributed Denial of Service (DDoS) attacks become more prevalent, organizations are leveraging tools such as Google Cloud Armor and Datadog to enhance their defense mechanisms. Google Cloud Armor serves as a cloud-native security service that protects applications by deploying security policies with rules to manage web traffic and preconfigured defenses against known threats. By integrating Datadog, organizations can monitor traffic patterns in real-time, collect Google Cloud Armor logs, and detect anomalies indicative of DDoS attacks. This integration allows for enhanced security monitoring through the use of Datadog's Cloud SIEM, which analyzes logs to detect signs of potential attacks and provides context for triggered alerts. By continuously analyzing these logs, organizations can fine-tune their security policies and develop effective protection and response strategies, ensuring resilience against evolving DDoS attack vectors.

Datadog has acquired Actiondesk, a cloud-based spreadsheet application that connects to live data sources and enables users to access and join data from across their cloud stack. The integration of Actiondesk into the Datadog platform aims to expand the ability of all teams within an organization to get more value out of their telemetry data by providing commonly used spreadsheet syntax for engineering, product, marketing, and finance teams to collaborate effectively.

Datadog is pleased to announce the acquisition of Actiondesk, a cloud-based spreadsheet application that integrates with live data sources, enabling users to access and join data from across their cloud stack. Datadog's acquisition of Actiondesk aims to expand the ability of all teams within an organization to get more value out of their telemetry data. The integration of Actiondesk's spreadsheet capabilities into the Datadog platform will enable engineering, product, marketing, and finance teams to collaborate on data-driven forecasting and business decisions using commonly used spreadsheet syntax.

The Datadog Software Catalog serves as a centralized platform for managing the performance, reliability, security, efficiency, and ownership of distributed services, fostering seamless DevSecOps workflows by eliminating knowledge silos. A key feature of this catalog is the Scorecards, which automatically evaluate services based on 10 predefined rules covering Production Readiness, Ownership and Documentation, and Observability Best Practices. However, recognizing that many engineering teams have unique service quality measures, Datadog allows users to create custom Scorecards to define their own evaluation criteria, addressing specific business needs not covered by the default rules. These custom Scorecards can formalize the adoption of internal tools, streamline manual processes like security reviews, and enhance service monitoring. By leveraging the Scorecards API, users can integrate these custom evaluations into the platform, facilitating the tracking of compliance with internal standards and ensuring a consistent quality across services. The process is straightforward, especially for APM customers, and new users are encouraged to explore the platform through a 14-day free trial.

This article discusses how Datadog manages incidents at scale, focusing on their entire process and the tools they've developed for handling them. They emphasize two core components of incident management: a culture of resilience and blameless organizational accountability, and monitoring their own systems. The company uses its own Incident Management tool to declare incidents, assign severity levels, set up communications channels, and designate first-line responders. They also rely on various support roles such as workstream leads, communications leads, and executive leads during incident response. Datadog prioritizes several metrics in order to gauge the success of their incident management process, including low rates of recurrence, increasing levels of incident complexity, decreased time to detection, and a low rate of spurious alerts.

Datadog's incident management process is designed to handle complex and dynamic systems. The company relies on a culture of resilience, blameless organizational accountability, and extensive structure and planning to manage incidents. Monitoring systems in real-time using tools like Datadog Incident Management, Teams, Service Catalog, and Workflow Automation enables teams to quickly identify and respond to issues. The incident management process involves identifying incidents, declaring them, triaging them, coordinating response efforts, guiding remediation, communicating with stakeholders, and declaring stabilization and resolution. Building resilience and maintaining transparency through regular training, analysis of lessons learned from incidents, and the use of tools like Datadog Notebooks are essential to the company's approach. Key metrics used to gauge success include low rates of recurrence, increasing levels of incident complexity, decreased time to detection, low rate of spurious alerts, and qualitative surveys for incident responders. The goal is to ensure that systems are steered towards greater reliability through effective monitoring, a proactive culture around incident management, and the development of purpose-built tools.