April 2023 Summaries

The EU General Data Protection Regulation (GDPR) has led to increased scrutiny and complex requirements for organizations when it comes to data privacy. Datadog, committed to upholding data privacy, has achieved the ISO 27701 Processor Certification for data privacy. This certification confirms that Datadog operates a Privacy Information Management System (PIMS) aligned with current laws and regulations related to personal information protection. The ISO 27701 Processor certification demonstrates Datadog's commitment to protecting the privacy of customers' observability and monitoring data, ensuring compliance with international data privacy requirements. This helps organizations maintain their own data privacy compliance programs while assuring customers that their personal information is well protected.

AWS Verified Access is a security service that enables organizations to grant local or remote access to corporate applications without using a VPN. It allows administrators to manage application access and administrative privileges at scale by assigning group policies. Datadog, as an SIEM partner for the launch of AWS Verified Access, has integrated Verified Access logs with its Cloud SIEM for analysis and real-time threat detection. This integration helps users visualize authentication activity, investigate Verified Access logs, detect suspicious trends in application authentication activity, and trace malicious actors using Cloud SIEM Investigator and detection rules. The Authentication Events dashboard helps visualize trends in security signals alongside other authentication-related events across all applications. Datadog's integration with AWS Verified Access enhances visibility across infrastructure and enables immediate action if a breach occurs.

AWS Verified Access is an easy and secure way to grant local or remote access to corporate applications without the need for a VPN. Organizations can assign group policies to manage application access and administrative privileges at scale, making it easier to monitor suspicious activity and detect security threats in real-time. With the integration of AWS Verified Access logs with Datadog Cloud SIEM, organizations can visualize authentication activity, investigate suspicious trends, and trace malicious actors' digital footprints across their AWS infrastructure. This integration enables DevOps engineers to quickly coordinate with security teams to verify whether failed events were false positives or truly stemmed from compromised credentials, ultimately enhancing security coverage and incident response capabilities.

Datadog has achieved the ISO 27701 Processor Certification for data privacy, demonstrating its commitment to protecting personal information and maintaining a Privacy Information Management System (PIMS) that aligns with current laws and regulations. This certification confirms Datadog's compliance with ISO 27001 and ensures that it has implemented comprehensive controls to protect customers' monitoring and observability data from unauthorized access or use. With this achievement, organizations can rely on Datadog's services while maintaining their own data privacy compliance programs, and Datadog continues to provide a secure and compliant solution for its customers.

The increasing complexity of modern software development lifecycles necessitates comprehensive monitoring solutions for continuous integration (CI) pipelines. Datadog's new TeamCity integration for CI Pipeline Visibility offers deep, end-to-end visibility into your TeamCity builds, helping you identify bottlenecks in your CI system, track and address performance regressions, and proactively improve the efficiency of your CI system. This enables data-driven decisions to increase the performance and reliability of pipelines, improving end-user experience by allowing teams to push code releases faster and with fewer errors. The TeamCity integration can be configured by downloading the Datadog CI plugin on the TeamCity server and adding specific parameters to your project. Once enabled, data from TeamCity pipelines will flow into Datadog, providing a high-level view of pipeline health and performance, as well as detailed information about individual builds and jobs. This helps identify problematic areas for optimization and troubleshoot failed builds or performance regressions.

Flagsmith, an open-source feature flagging and remote configuration service, helps developers release new features faster by allowing them to easily roll out and test features for specific user subsets without needing to deploy new code. To improve visibility into issues caused by feature flags, Datadog has partnered with Flagsmith to offer a Flagsmith Datadog App, integration, and software license via the Datadog Marketplace. This partnership enables users to track the status of feature flags, record flag events as audit logs, and enrich Real User Monitoring (RUM) telemetry with data collected from Flagsmith for easier troubleshooting and monitoring of application performance.

Datadog Data Streams Monitoring (DSM) is a tool that helps track and improve the performance of streaming data pipelines and event-driven applications using Kafka and RabbitMQ. It provides deep visibility into services, queues, and infrastructure across the entire pipeline, enabling users to monitor latency, pinpoint bottlenecks, identify issues such as blocked messages or offline consumers, and enhance existing troubleshooting workflows. DSM automatically maps the architecture of streaming data pipelines and visualizes interdependencies, service ownership, and key health metrics across services and infrastructure. This helps users monitor end-to-end latency, alert on bottlenecks, identify and remediate floods of backed-up messages, and improve overall pipeline performance.

Nicholas Thomson, Jane Wang, and Jonathan Morin discuss the challenges of managing streaming data pipelines that use technologies like Kafka and RabbitMQ. The authors argue that SREs and application developers often struggle to determine if these pipelines are performing as expected due to a lack of visibility into every component of the pipeline. Datadog Data Streams Monitoring (DSM) helps address this issue by providing end-to-end latency, throughput, and consumer lag metrics for streaming data pipelines and event-driven applications. DSM enables users to monitor latency on services, pinpoint faulty producers, consumers, or queues driving latency and lag, discover hard-to-debug pipeline issues, root-cause and remediate bottlenecks, and quickly see who owns a pipeline component for immediate resolution. The tool automatically maps the architecture of your entire streaming data pipeline, including visualization of interdependencies, service ownership, and key health metrics across services and infrastructure dependencies. By using DSM, users can monitor and alert on end-to-end latency, pinpoint the root causes of bottlenecks in pipelines, identify and remediate floods of backed-up messages, enhance existing troubleshooting workflows, and take advantage of deep visibility into their streaming data pipelines.

Datadog has released a new integration with TeamCity, allowing developers to gain deep visibility into their continuous integration (CI) pipelines and identify bottlenecks in real-time. The integration enables data-driven decision-making to improve the performance and reliability of CI systems, ultimately leading to faster code releases and reduced errors. To configure the integration, users need to download the Datadog CI plugin on the TeamCity server, set up parameters such as API keys and site URLs, and enable the integration in their project settings. Once enabled, data from TeamCity pipelines flows into Datadog, providing a comprehensive view of pipeline health and performance. Users can investigate pipeline failures, identify performance regressions, and optimize build chain performance using various features such as pipeline overview pages, timeseries widgets, and flame graph views.

The text discusses the challenges of releasing new features quickly and the benefits of using Flagsmith, an open-source feature flagging and remote configuration service. It highlights how Flagsmith can help developers roll out and test new features without deploying new code, and how it integrates with Datadog to provide visibility into feature flags and their impact on application performance. The text also explains how the integration enables users to track feature flag status directly from Datadog, stay updated on changes to feature flags with audit logs, and enrich Real User Monitoring (RUM) telemetry with feature flag data for deeper visibility into user sessions. By combining Flagsmith's feature management capabilities with Datadog's real-time monitoring, users can detect issues and improve overall application performance.

Datadog has introduced Log Pipelines to help large organizations manage their logs more effectively. The fully managed service allows users to ingest logs from various sources, parse and enrich them with contextual information, add tags for usage attribution, generate metrics, and quickly identify log anomalies. Additionally, the new feature supports Log Forwarding to custom destinations, enabling users to centralize log processing while accommodating flexible workflows and enabling compliance by duplicating logs across locations. This helps organizations maintain standardized logs across teams and platforms, streamline collaboration, and ensure accuracy of local backups.

Azure App Service is a fully managed platform-as-a-service (PaaS) solution that enables developers to quickly build and release services without worrying about infrastructure management. Microsoft has recently released Web App tracing and custom metrics for Azure App Service applications running in Linux environments, supporting various runtimes such as .NET, Node.js, Java, Python, and PHP. This feature allows users to capture distributed traces across their Azure App Service Linux web applications, write and submit custom metrics, and gain quick insights through the Azure Serverless view. Datadog's APM automatically tags errorful spans for surfacing emergent issues, while enabling custom metrics for monitoring specific business KPIs. The Serverless view provides a comprehensive overview of the entire Azure App Service architecture, allowing users to monitor their Linux web applications in context and troubleshoot performance issues efficiently.

Azure App Service is a fully managed platform-as-a-service solution that enables developers to quickly build and release web applications, event-driven functions, RESTful APIs, and more. The service provides dynamic scaling without worrying about provisioning and maintaining infrastructure. Recently, Azure App Service has been extended to support Linux-based environments with various runtimes such as .NET, Node.js, Java, Python, and PHP. This extension allows developers to capture distributed traces across their web applications, write and submit custom metrics, and gain quick insights into their applications' performance using the Datadog APM and Azure Serverless view. With these features, users can troubleshoot issues more efficiently by visualizing flame graph spans, tagging errorful spans, and filtering resources in the Serverless view.

Datadog Teams is a newly introduced feature designed to enhance collaboration, accountability, and visibility within complex organizational structures by allowing users to organize resources and teams on the Datadog platform. As organizations grow and their operations become more intricate, Datadog Teams aims to streamline communication and resource management by providing curated views of crucial data, improving the signal-to-noise ratio and reducing alert fatigue. Through its integration with the Datadog Service Catalog, it helps manage microservice environments by clarifying resource ownership, thereby fostering collaboration across teams, especially during incident management. The platform facilitates scoped visibility, enabling teams to access only the data they need, which promotes productivity and agility while minimizing the risk of information overload. Future enhancements will include further integration with popular identity providers, ensuring that as organizations evolve, their teams remain synchronized.

Log Management is an essential tool for organizations to gain insights into their environment's health, security, and performance. It helps in tasks such as auditing, performance analysis, incident response, and security investigations. Datadog's Log Management provides a seamless search experience that enables users to build efficient log queries quickly. Its features include autocomplete queries, reuse of recent searches and saved views, keyboard shortcuts, syntax highlighting, and error messages for troubleshooting. These capabilities help teams investigate logs more efficiently during time-critical scenarios like security breaches or service outages.

The integration of Datadog with GitHub's Deployment Protection Rules allows users to create health checks for deployments and prevent faulty releases from affecting their entire system. This feature enables controlled releases by allowing users to stagger deployments and test functionality in isolated parts of the system before a wider release. Customized quality gates can be used to validate service health and resource consumption at every stage of deployments, helping assess the performance of new code under real conditions. Datadog's integration with GitHub Deployment Protection Rules provides notifications for passed and failed checks, allowing users to determine exactly where issues in their stack occurred and troubleshoot them before releasing across all environments.

Datadog Log Management provides a point-and-click log search experience that enables teams to efficiently investigate logs, build and reuse queries, and ensure accuracy. The tool offers features such as autocomplete queries, recent searches and saved views, keyboard shortcuts, syntax highlighting, and error treatment to speed up investigations and reduce errors. It also supports raw mode for experienced users to navigate by typing queries, making it easier to interpret complex queries and troubleshoot issues. By using Log Management's search features, teams can build complete and accurate log queries quickly, reducing time-to-insight and improving overall efficiency.

Kassen Qian and Will McMullen from Datadog are proud to join GitHub as launch partners for the public beta release of Deployment Protection Rules, a feature that enables organizations to stagger their deployments and test functionality in isolated parts of their system before a wider release. This feature provides health checks for deployments to specific services or environments, allowing users to prevent faulty releases from spreading to their entire system. The integration with Datadog enables users to create monitors that perform automated checks on gated deployments to assess any impacts to their services and application. With this combination, organizations can not only prevent faulty deployments from impacting their users but also gain insight into exactly how they might affect their system. By using customized quality gates, users can ensure the health of their releases by validating service health and resource consumption at every stage of their deployments. The integration allows users to track performance issues, troubleshoot problems before releasing across all environments, and create monitors that are uniquely tailored to their deployment checks. With this combination, organizations can deploy safely with GitHub and Datadog, ensuring a smooth end-user experience.

Datadog Workflows, now available in the Service Management and Integrations menus, can be used with Cloud SIEM to automate repetitive security tasks such as triaging security signals or detecting emerging vulnerabilities. This integration helps reduce the burden on security engineers by allowing them to focus on more complex tasks, and assists teams in staying ahead of novel threats by automating the classification of emerging vulnerabilities. The combination of Cloud SIEM and Workflows also integrates with Datadog Case Management, which provides a centralized workspace for investigating security signals, streamlining triage and troubleshooting processes. These integrations help teams reduce tool sprawl and security spend by unifying automation, case management, and SIEM capabilities in a single pane of glass.

Datadog Case Management provides a centralized platform for tracking, triaging, and troubleshooting issues in complex systems. It helps organize investigations by creating cases from alerts, security signals, and error-tracking issues, allowing users to assign cases to specific teams or individuals. The platform also enables linking of graphs, logs, and other telemetry data with information from external tools such as messaging and issue-tracking apps. With features like custom inboxes, filters, and prioritization options, Datadog Case Management streamlines the process of handling back-burner issues and helps prevent customer impact.

Datadog Cloud SIEM is a security operations center (SOC) tool designed to detect and remediating security threats in DevSecOps environments. It automates repetitive security tasks such as triaging security signals or detecting emerging vulnerabilities, reducing the burden on security engineers and helping teams stay ahead of novel threats. Datadog Workflows can be used in concert with Cloud SIEM to automate these tasks, integrating with Case Management for a centralized workspace and providing insights from Datadog's dedicated security research team. Workflow blueprints are available to help identify, retro-hunt, and create detection rules for emerging vulnerabilities, while also automating common security tasks such as triage and analysis of security data. This integration enables teams to focus on critical security issues and leverage Datadog's wealth of security research experience to detect and defend against unforeseen threats.

Datadog Case Management is a centralized ticketing system that helps organizations track, triage, and troubleshoot issues more efficiently. It enables teams to prioritize and delegate cases from within one view, organize investigations using an observability-enhanced source of truth, and streamline collaboration across the organization. With Datadog Case Management, users can create cases directly from telemetry data, assign ownership, and configure notifications based on customized views. The system is designed for teams of any size and provides a single view to track all issues, ensuring that every problem is properly addressed. It also integrates with other tools such as ServiceNow and Jira, providing easy access to relevant information across platforms.

Developers often spend a significant portion of their time fixing bad code, which can slow down innovation and negatively impact productivity. Codiga is a static code analysis tool that helps developers write better code faster by monitoring it in real-time during the development process. It supports various popular languages and frameworks and integrates with major IDEs and Git providers. Datadog's acquisition of Codiga aims to enhance its observability platform, providing users with improved software development lifecycle support for maintaining high-quality software more efficiently.

The text discusses how Datadog developed a new query and render scheduler to optimize Dashboard performance. Initially designed for Dashboards, the scheduler was then generalized for use on any expensive task or fetch-heavy application. It consists of two modules: query scheduling and render scheduling. The query scheduler determines when data fetches occur, while the render scheduler controls when widgets are rendered. Both were initially heuristic-based but have been simplified to improve performance and reduce complexity. The new algorithm for query scheduling is governed by only six parameters and has resulted in a better distribution of tasks compared to the old algorithm. Meanwhile, the render scheduling algorithm uses the Browser Scheduling API to prioritize tasks based on their relevance and the browser's resources. This has significantly reduced the number and duration of widget renders, leading to a faster and more responsive UI. The team plans to continue monitoring performance metrics and tuning parameters for further optimization.

Codiga, a tool that provides powerful static code analysis, is joining Datadog as part of the latter's acquisition. Codiga helps developers write better code faster by monitoring and analyzing their team's code in real-time, enabling them to identify and address errors before they become major issues. The integration of Codiga into the Datadog platform aims to provide a comprehensive observability platform that caters to every aspect of the software development lifecycle. As a result, users can expect an enhanced experience that helps them develop and maintain high-quality software more efficiently than ever before.

The new query and render scheduler was initially developed to optimize performance on dashboards but was later generalized for use on any expensive task or fetch-heavy application. The original scheduler had a complex set of heuristics governing its behavior, which made it difficult for developers to reason about how a dashboard updated and rendered its content. A simpler algorithm was eventually arrived at, with only 6 parameters governing its behavior, and this new algorithm performed better than the old one in terms of task distribution. The render scheduler was also improved using the Browser Scheduling API, which allows for prioritized tasks that are natively scheduled by the browser. This resulted in faster widget renders and fewer long tasks, leading to a faster and more responsive UI.

In this article, the author discusses best practices for securing access to and from cloud environments. They begin by explaining identity and access management (IAM) in the context of cloud security, which involves managing digital identities and their level of access to resources within an environment. The AAA model—comprising authentication, authorization, and accounting—is used as a framework for creating efficient access control. The article then delves into specific best practices for strengthening IAM systems: treating identities as a new kind of boundary, using complex passwords and multi-factor authentication for user accounts, limiting the use of static, long-lived credentials for service accounts, organizing identities into logical groups, assigning permissions based on zero-trust and least-privilege principles, and monitoring IAM activity using logs. The author emphasizes the importance of regular auditing to identify orphaned user accounts and other vulnerabilities within an organization's environment. They also recommend enforcing strong passwords and MFA for user accounts, as well as leveraging cloud provider-based identity management services to replace static credentials for service accounts. Furthermore, the article highlights the significance of organizing identities into logical groups based on their role or function, which enables efficient management of permissions at a high level. The author also discusses implementing zero-trust and least privilege controls for IAM by considering factors such as who should access a resource, how they should access it, when they should be allowed to access it, why they need access, what data they should be allowed to access, and where they should be allowed to access it from. Lastly, the article underscores the importance of monitoring IAM activity using logs, which provide valuable insights into user behavior within an environment. The author suggests using centralized logging tools and Cloud SIEM platforms to efficiently identify security threats and build identity-centric monitoring workflows.

Treat identities as a new kind of network boundary by shifting focus from where traffic comes from to who or what is accessing an environment, and regularly audit identities to visualize the boundaries of their environment with improved accuracy. Use complex passwords and multi-factor authentication for user accounts to protect them from account takeovers and other threats. Limit the use of static, long-lived credentials for service accounts to reduce the attack surface and increase security. Organize identities into logical groups based on their role or function to manage permissions at a high level and provide context around who is accessing a resource. Assign permissions as needed, based on zero-trust and least-privilege principles, to systematically deploy the right permissions at every level of cloud infrastructure. Monitor IAM activity using logs to capture key information about user activity and identify potential security threats.

DevOps and security engineers in highly regulated industries use compliance standards to quickly identify issues in their cloud environments. Datadog Cloud Security Management provides over 250 out-of-the-box rules that align with industry-standard frameworks like SOC 2, PCI-DSS, and ISO. Users can modify existing rules or create new ones tailored to their organization's needs. The platform supports writing detection rules using Rego, a query language for policy-as-code workflows. It also allows testing of rules before publishing and customizing detection alerts with severity levels and automatic notifications via popular communication channels like Slack and Jira.

Ansible is a powerful automation tool that helps organizations efficiently manage and configure a large number of hosts, specifically focusing on automating the installation of the Datadog Agent on Windows hosts within a dynamic inventory, such as Amazon EC2 instances. The process involves setting up Ansible on a central control node, building an inventory of managed nodes using a combination of static and dynamic sources, and creating and running playbooks that assign repeatable tasks to these nodes. This guide demonstrates how to configure Datadog integrations, including SQL Server and Windows Event Logs, and how to enable Live Process monitoring to gain deep visibility into system metrics and application performance. Ansible's flexibility allows it to work with other platforms and tools, offering scalability and adaptability for managing cloud infrastructure. By automating these processes, organizations can efficiently monitor and optimize their infrastructure, ensuring robust data collection and analysis capabilities.

Datadog Cloud Security Management provides more than 250 out-of-the-box rules that are mapped to industry-standard compliance frameworks like SOC 2, PCI-DSS, and ISO. These rules can be cloned, modified, or created to match an organization's cloud security practices, regardless of the cloud provider. The platform enables developers and security engineers to quickly surface misconfigurations in their cloud environments, enforce customized standards, create security-related alerts on resource configurations, and focus on the findings that matter most to their organization. With Datadog Cloud Security Management, users can write rules for detecting misconfigurations in cloud resources using Rego, test detection rules against cloud infrastructure before publishing them, customize detection alerts and route them to the appropriate teams for remediation, and fine-tune rules for detecting misconfigurations across various cloud environments.

Organizations in regulated industries must address cloud misconfigurations to maintain customer trust and privacy, and Datadog Cloud Security aids this by offering more than 250 pre-existing detection rules that align with compliance frameworks such as SOC 2, PCI-DSS, and ISO. These rules can be cloned, modified, or created from scratch using the Rego query language to meet specific business or security objectives, allowing for the enforcement of customized standards and the generation of security alerts tailored to resource configurations. Datadog provides tools for testing and validating rule logic against cloud resources before publishing, ensuring accurate detection of issues like obsolete IP addresses or open database ports. Additionally, users can configure alerts to include severity levels, remediation steps, and route notifications through channels like Slack and Jira to ensure timely and efficient responses by the appropriate teams. By leveraging Datadog’s capabilities, security and DevOps teams can quickly understand and mitigate the impact of threats on cloud resources, with options for a free 14-day trial for new users.

AWS Lambda Function URLs simplify the process of creating serverless applications connected to and invoked from the web by enabling HTTP/S requests to trigger Lambda functions. Datadog, in partnership with AWS, provides end-to-end visibility into requests to functions triggered by URLs and key metrics related to Function URLs and Response Streaming. This allows users to monitor their serverless environment effectively and maintain optimal performance for their applications.