August 2023 Summaries

In June 2023, Google enhanced its Vertex AI platform, which provides AI and machine learning services, by incorporating generative AI support, allowing users to test and deploy large language models (LLMs). Datadog has announced an integration with Vertex AI, enabling users to monitor the health and performance of their LLM-powered services in production. This integration provides insights into key metrics such as network traffic, prediction errors, latency, and resource utilization through an easy-to-use dashboard. Users can track the standard RED metrics—rate, errors, and duration—to assess model performance and infrastructure health, and receive real-time alerts for anomalies in metrics like memory and CPU usage. The integration aims to offer a comprehensive view of AI deployments, helping users quickly identify and address issues, and ensuring efficient operation of generative AI-powered services.

Blink is a no-code security platform that streamlines workflows for Security and DevOps engineers by automating tasks such as incident response, honeypotting, recovery, and remediation. Datadog now offers an out-of-the-box Blink integration and software license through the Datadog Marketplace. This integration allows users to create workflow automations that enhance infrastructure monitoring, cyber incident response, and enrich Datadog alerts. By leveraging native Datadog actions, such as creating a monitor or querying metrics, users can automate processes and better enforce security guardrails. The Blink integration complements other Datadog features like Incident Management, Cloud Security Monitoring, SIEM, Infrastructure Monitoring, Container Monitoring, and more.

Nicholas Thomson, Security and DevOps engineers often spend a lot of time creating complex workflows. Blink is a no-code security platform that enables users to create workflow automations, triggers, and self-service apps to streamline processes. Datadog now offers an out-of-the-box Blink integration and software license via the Datadog Marketplace, allowing joint users to enhance their infrastructure monitoring and incident response workflows. The integration helps reduce time and effort required for security and DevOps teams to create a more dependable and secure system. With the Blink integration, users can automate infrastructure monitoring as they scale, enhance cyber incident response by automatically triggering Datadog actions, enrich their Datadog alerts with Blink workflow automations, and trigger workflow automations in Blink from Datadog events or native Datadog actions from Blink workflows.

Google Cloud Run is a managed platform that allows users to deploy, manage, and scale workloads using serverless containers. Datadog's integrations with Google Cloud Run and Google Cloud Platform enable the collection and visualization of metrics from containerized workloads as well as application and platform logs from Google Cloud Run. The Serverless view in Datadog provides comprehensive visibility into Cloud Run services, allowing users to monitor enhanced and custom Cloud Run metrics, investigate issues in serverless workloads with metrics, traces, and logs, and visualize their Cloud Run telemetry.

Datadog's adoption of Cloud Security Management (CSM) has significantly improved its ability to manage security risks in its complex cloud environment. CSM provides increased visibility into risk across the infrastructure, enabling teams to streamline remediation and collaborate more effectively with engineering to maintain a healthy security posture. The platform offers customizable dashboards that provide a consolidated view of misconfigurations, allowing teams to prioritize issues based on severity and communicate about their security posture with multiple stakeholders across the organization. Additionally, CSM enables mapping of misconfigurations to specific engineering teams responsible for affected resources, facilitating quick identification of relevant contacts for mitigation or remediation. The platform also offers detailed side panels with information on how to configure assets to more secure states and automates response through one-click remediation. Overall, Datadog's use of CSM has greatly enhanced its ability to manage security risks in a large-scale environment.

The complexity of microservice architectures often makes it difficult to determine service dependencies and ownership. This can hinder accountability, impede application development, incident investigations, and onboarding processes. Datadog Service Map now allows visualization of service ownership and application boundaries, helping organizations quickly grasp their evolving system architecture. The map helps streamline development and oversight of applications by providing a clear picture of service dependencies and interconnections. It also enables users to quickly home in on key service data during incidents, improving incident response coordination. By visualizing service ownership and application boundaries, the Datadog Service Map centralizes expertise on microservice architectures, streamlining development, incident management, and onboarding processes.

The Datadog Service Map is a visualization tool that helps organizations understand their microservice architectures by providing a clear picture of service ownership and application boundaries. It enables teams to quickly grasp their evolving system architecture, streamline development and oversight, quickly identify key service data during incidents, and centralize expertise on their services. By visualizing service ownership and application dependencies, the Service Map helps prevent accountability challenges, impede incident investigations, and prolong onboarding processes.

Datadog's Cloud Security Management (CSM) solution helps manage complex cloud environments by providing a unified security view through shared dashboards, understanding which teams own misconfigured resources so they can be notified, streamlining the process of prioritizing and remediating misconfigurations, and offering a consolidated view to identify and remediate risks. CSM enables organizations like Datadog to monitor their infrastructure resources, identify critical misconfigurations, prioritize remediation efforts, automate response with one-click remediation, and mute non-critical findings. By leveraging these capabilities, internal security, risk, and engineering teams can collaborate more effectively to improve the organization's security posture.

In complex cloud environments, managing infrastructure and resource configurations is challenging, especially with compliance requirements like PCI and HIPAA. Datadog addresses these challenges by using Datadog Cloud Security, which enhances visibility and streamlines collaboration between security and engineering teams. This tool provides a unified security view through shared dashboards, allowing teams to prioritize and remediate misconfigurations effectively. With over 400,000 infrastructure resources and 7,000 services, Datadog can map misconfigurations to specific engineering teams, enabling timely and efficient remediation. The platform offers features like the Security Inbox for identifying critical issues and Workflow Automation for one-click remediation, improving the overall security posture by allowing engineers to self-serve and focus on high-priority risks. Additionally, the Mute function is used to manage misconfigurations that do not require immediate action, enhancing reporting and communication within the organization.

Nathaniel Beckstead discusses the importance of ensuring that threat detection rules function properly within security programs, highlighting the challenges faced in container and cloud environments. While Red Canary’s Atomic Red Team offers reliable detection tests for traditional systems, its application in containerized setups often demands significant manual effort. Datadog’s Workload Security Evaluator addresses this issue by streamlining the process of running Atomic Red Team tests in container environments, offering an automated platform that simplifies setup and execution. This tool makes it easier to validate detection coverage against MITRE ATT&CK techniques, particularly for cloud-based workloads, by providing a ready-to-use container environment monitored by the Datadog Agent. By facilitating efficient and consistent security testing, the Workload Security Evaluator aids organizations in maintaining robust threat detection systems.

The text discusses the integration of Sigma rules with Datadog Cloud SIEM to enhance security detection capabilities. Sigma, an open-source project, provides standardized detection rules to tackle a wide range of threat scenarios, benefiting from community expertise. The integration allows security teams to convert Sigma rules into Datadog's format, facilitating early-stage threat detection without creating detection logic from scratch. It involves using the Sigma CLI and Datadog plugin to convert and verify rules, ensuring accurate field mappings within Datadog's system. Once converted and validated, the rules can be imported into Datadog Cloud SIEM, allowing teams to modify and tailor them to their specific environment. This integration aims to improve detection coverage and expertise, with resources available for current and new Datadog users to get started.

Datadog has introduced new offerings in Cloud Security Management (CSM) with CSM Pro and CSM Enterprise. These packages provide tailored security capabilities to meet specific organizational requirements. CSM Pro offers continuous scanning for misconfigurations and vulnerabilities, while CSM Enterprise provides real-time visibility into workloads and file activity. Existing customers will be migrated based on their current product usage, with no impact on Cloud Workload Security (CWS) users. New customers will not be able to purchase Cloud Security Posture Management (CSPM) starting in October 2023.

AWS Systems Manager (SSM) offers a marketplace of pre-packaged software scripts for SSM-managed Windows and Linux instances, enabling users to automatically install custom software on large groups of instances. Datadog now provides documents that enable easy, one-click installation of the latest version of its Agent for both Linux and Windows through the AWS SSM marketplace. This allows joint Datadog and AWS users to install the Agent without having to configure the Agent YAML file, saving them time and energy. The one-click installation also offers an easy way to automate Agent updates across a distributed system. Additionally, once installed, the Datadog Agent provides out-of-the-box dashboards for monitoring AWS services and can be combined with Datadog's AWS integration for more granular tagging and alerting capabilities.

Datadog has introduced new offerings in its Cloud Security Management product: CSM Pro and CSM Enterprise, designed to meet specific requirements of organizations for securing their environments. CSM Pro provides continuous security scanning of cloud and container environments, while CSM Enterprise offers real-time visibility into production workloads and file activity, enabling teams to detect and correlate threats more effectively. Existing customers will be migrated to the new offerings based on their current product usage, with some customers automatically transitioning to CSM Pro or CSM Enterprise by November 1, 2023.

Nicholas Thomson, Shanel Huang, and Lutao Xie from AWS discuss how to use AWS Systems Manager (SSM) to install the Datadog Agent with one-click installation. This allows users to automatically install custom software on large groups of instances without having to configure the Agent YAML file. The process involves configuring an IAM role, searching for the Datadog document in the SSM marketplace, and selecting the target instances to install the Agent on. Once installed, users can monitor their AWS infrastructure with out-of-the-box Agent capabilities, including dashboards for EC2, RDS, Lambda, S3, and more. The Agent also offers customizable widgets and integrations with other services, such as OpenAI and Azure, allowing users to get the most out of Agent-based AWS monitoring. Additionally, the one-click installation automates updates across a distributed system, making it easier to roll out new versions of the Agent without manual configuration.

Service level objectives (SLOs) are essential for organizations to measure product reliability and ensure positive end-user experiences. They help businesses understand tradeoffs required to meet specific targets and set clear boundaries between teams. However, setting realistic SLOs is crucial as unrealistic standards can lead to burnout or disregard of the objectives. SLOs can determine issue severity, delineate component responsibilities, and guide business priorities. Collaboration between stakeholders like Customer Success Managers (CSMs) and engineers is vital for setting effective SLOs that align with business goals and technical capabilities. Defining "working" is crucial when creating SLOs, as it depends on the context and use case. Understanding user expectations and their level of performance acceptance helps determine what "working" means in a given scenario. By asking key questions and collaborating effectively, organizations can set effective SLOs to bolster service reliability.

To create effective service level objectives (SLOs) that gauge the reliability of a product, organizations should consider what SLOs can and cannot do for them. Setting realistic SLOs based on current capabilities is crucial to avoid engineers burning themselves out or ignoring SLOs altogether. SLOs can help determine the severity of an issue, delineate clear boundaries between components, and guide business priorities by assessing how well they are meeting SLOs. It's essential to consider different stakeholder perspectives, such as customer success managers and technical teams, to ensure effective collaboration and alignment. The definition of "working" will also change depending on the context and use case, so it's critical to clearly define what "working" means in a given context. Understanding who the users are and what level of performance they expect from the service is vital to setting effective SLOs.

CoreDNS is an open source DNS server that provides service discovery within a Kubernetes cluster and can resolve requests for internet domain names. Monitoring CoreDNS performance metrics, such as request processing latency and update latency, can help detect potential issues affecting the overall performance of your applications running on Kubernetes. Additionally, monitoring resource usage metrics like memory utilization and CPU utilization can ensure that your autoscaling parameters and container resource requests and limits are appropriate for managing CoreDNS workloads effectively.

This article details how to collect and visualize metrics from CoreDNS servers using the Prometheus plugin, which exposes metrics in the OpenMetrics format by default in Kubernetes clusters. It explains how to query the CoreDNS /metrics endpoint for a snapshot of performance and activity metrics and describes how Prometheus can store these metrics for historical analysis, while Grafana can be used to visualize and monitor trends. The document also highlights the importance of CoreDNS logs for deeper visibility into server activity, despite potential performance impacts from query logging, and provides guidance on handling these logs, including plugin-specific and status logs for troubleshooting and root cause analysis. Additionally, it mentions using tools like Datadog for comprehensive monitoring and alerts on CoreDNS metrics and logs, with a promise of further exploration in the next part of the series.

The text provides a comprehensive guide on using Datadog to monitor CoreDNS in Kubernetes environments, focusing on metrics, logs, and traces to ensure optimal performance. It outlines the steps to integrate CoreDNS with Datadog, including the installation and configuration of the Datadog Agent and Cluster Agent using Helm, a package manager for Kubernetes. The guide explains how to visualize CoreDNS metrics through Datadog's dashboard, use tags for data analysis, and monitor container resource usage and CoreDNS performance with Network Performance Monitoring (NPM) and distributed tracing. It also details how to collect and explore CoreDNS logs using Datadog's Log Management, enabling users to correlate logs with metrics and traces for a holistic view of their Kubernetes infrastructure. Overall, the text emphasizes the importance of monitoring CoreDNS as part of maintaining the health and performance of Kubernetes applications and highlights the capabilities of Datadog in providing integrated visibility across the stack.

CoreDNS is an open-source DNS server that can resolve requests for internet domain names and provide service discovery within a Kubernetes cluster. It's the default DNS provider in Kubernetes as of v1.13, simplifying cluster networking by enabling clients to access services using DNS names rather than IP addresses. Monitoring CoreDNS is crucial to ensure that elevated latency or error rates are not disrupting communication among your services and causing bottlenecks in your application.

AWS Step Functions is a service that simplifies the management of distributed applications by abstracting them into state machines. Datadog, an observability and monitoring platform, now supports native integration with AWS Step Functions, allowing users to monitor their state machines alongside other infrastructure components. This integration provides comprehensive visibility into the performance of state machines and Lambda functions, enabling users to identify issues and errors quickly. With this support, users can also set up alerts for unexpected failures or slow execution times in their state machines. Additionally, Datadog's integration with AWS services like Amazon Simple Queue Service and Amazon SageMaker allows users to inspect every stage of their workflows within a single platform.

Organizations face challenges securing their cloud infrastructure and services due to the increasing size of their footprint and complexity of applications. Security breaches often go undetected for months, making it difficult to access logs that provide a complete history of an attack. Datadog's Cloud SIEM Investigator enables organizations to visualize security logs, detect patterns in large volumes of log data, and manage attacks with Workflow Automation. This helps teams understand the scope and impact of an attack, regardless of when it occurred, and respond quickly to mitigate risks.

Datadog has released several new features and updates across its product suite to enhance observability, improve security posture, and streamline developer workflows. These include the integration of AWS Security Hub findings into Cloud SIEM, enhanced capabilities for API Management, a new mobile app testing solution, and more. Additionally, Datadog has expanded support for serverless environments on Azure and Google Cloud Platform, as well as added new features to its network monitoring and database monitoring offerings.

Datadog Application Vulnerability Management provides real-time, code-level vulnerability detection based on application traffic without requiring scans or security tests. It helps organizations prioritize critical vulnerabilities with meaningful risk assessments and use source code insights to accelerate remediation. By using an Interactive Application Security Testing (IAST) approach, it identifies vulnerabilities within your application code during runtime, enabling quick identification of potential issues. The tool also provides a Remediation tab for suggested fixes and integration with GitHub, IntelliJ, or VS Code to make these fixes. This helps organizations focus their triage efforts and eliminate the noise that can come from leaving out runtime context, reducing the risk of security incidents.

The Datadog Ambassadors program recognizes developers who share their expertise and solutions with the wider community, helping others build observability into their applications and infrastructure. Members are handpicked based on their contributions to the community and can reapply or be nominated by existing ambassadors. Benefits include exclusive opportunities, free certification exams, and more. The inaugural group of Datadog Ambassadors includes experts in DevOps, cloud computing, observability, and other technical fields who have contributed to the community through blog posts, open-source contributions, and more.

Datadog has introduced two new cloud security capabilities within its Cloud Security Management (CSM) platform - Cloud Infrastructure Entitlement Management (CIEM) and Vulnerability Management. CIEM helps identify and address identity risks in IAM configurations, while Vulnerability Management leverages infrastructure observability to continuously scan containers and hosts for vulnerabilities. These features aim to provide comprehensive visibility, real-time threat detection, and continuous configuration audits across the entire cloud infrastructure, enabling efficient identification, prioritization, and mitigation of security threats.

NVIDIA is the leading company in artificial intelligence (AI) and high-performance computing due to its advanced discrete graphics processing units (GPUs). GPUs are essential for handling parallel computing tasks, which are crucial for AI applications. Datadog has integrated with NVIDIA Data Center GPU Manager (DCGM) Exporter to provide seamless monitoring of GPU performance alongside the rest of an organization's AI stack. This integration allows users to visualize GPU health, identify bottlenecks in GPU resources, and track GPU power usage for cost management purposes. The integration is available with Datadog Agent version 7.47+ and can be configured using templates provided by Datadog.

As infrastructure and applications scale, managing observability data becomes increasingly complex. Datadog's managed Log Pipelines and Observability Pipelines offer flexibility in log management, allowing users to experiment with new vendor solutions without disrupting production workflows. Managed Log Pipelines are fully managed and can be customized for data ingestion, processing, and routing, while Observability Pipelines provide granular control over observability data and meet data residency and compliance requirements. Both pipelines enable dual shipping of logs to multiple destinations, providing flexibility and control in long-term log management.

Flex Logs is a log management solution introduced by Datadog that centralizes all logs within one platform regardless of the use case. It decouples storage costs from querying costs, allowing for long-term retention without sacrificing visibility or performance. Flex Logs offers three tiers: Standard Indexing for frequently queried data, Archiving for long-term retention and infrequent queries, and Flex for high volume logs that require occasional urgent queries. It provides log retention at a commodity storage price point of $0.05 per million events per month without impeding querying. This solution enables organizations to manage growing volumes of logs while maintaining quick access to the data they need.

Datadog Code Security is a tool that helps organizations identify and prioritize code-level vulnerabilities in real-time, without requiring manual configuration or external security tests. It uses an Interactive Application Security Testing (IAST) approach to monitor application code during runtime, providing critical threat data and risk scores that enable quick triage of the most critical security issues. The tool also offers source code insights, such as filename, line number, and method, alongside suggested remediation actions to accelerate fix efforts. By detecting vulnerabilities in production before they can be exploited, Datadog Code Security helps reduce the risk of security incidents and maintain user trust.

Datadog's Cloud SIEM Investigator is a tool designed to help organizations conduct historical security investigations, enabling them to understand the scope and impact of an attack, regardless of when it occurred. By visualizing logs, finding patterns in Security Signals, and managing attacks with Workflow Automation, teams can gain insights into security breaches, identify malicious actors, and respond quickly to contain and remediate incidents. Cloud SIEM Investigator supports AWS CloudTrail logs and Google Cloud Audit Logs, providing a cost-effective way to store a deep history of logs and enabling organizations to proactively monitor their cloud infrastructure for threats.

Datadog's Cloud Security Management (CSM) offers comprehensive visibility, real-time threat detection, and continuous configuration audits across cloud infrastructure. The new capabilities of CSM, Datadog Cloud Infrastructure Entitlement Management (CIEM) and CSM Vulnerability Management, enable teams to identify and address identity risks and infrastructure vulnerabilities, respectively. Datadog CIEM secures the environment from IAM-based attacks by leveraging industry best practices and security research recommendations, prioritizing work based on identity risks or at-risk resources, and providing detailed insights for efficient mitigation. Meanwhile, CSM Vulnerability Management detects and helps teams address infrastructure vulnerabilities using context-based prioritization and seamless end-to-end visibility into infrastructure vulnerabilities.

Datadog has introduced Bits AI, a generative AI-powered DevOps copilot designed to help users investigate and respond to incidents more efficiently across the Datadog web app, mobile app, and Slack. Bits AI provides a single conversational interface that surfaces insights from throughout the environment by correlating key data from various sources, including Watchdog-detected log and trace anomalies, metrics, events, real-user transactions, Security Signals, and cloud costs. It can also suggest automated code fixes, create synthetic tests, and find relevant Datadog workflows to trigger. Bits AI helps users diagnose issues and determine their scope, investigate issues faster by surfacing key data, streamline incident response and remediation, prevent issues from reoccurring, and power up their monitoring workflows. The tool is currently available in Preview and can be requested or signed up for a free trial to get started.

NVIDIA has become the leader in the artificial intelligence space due to its high-performance capabilities and discrete graphics processing units (GPUs), which account for approximately 80 percent of the market share for production-level AI, gaming, graphics rendering, and other complex data processing tasks. The company's integration with Datadog provides customers with increased visibility into their AI stack, enabling them to monitor NVIDIA's GPU performance alongside other critical parts of their AI stack. This integration allows organizations to visualize the health of their GPUs, identify bottlenecks in GPU resources, track GPU power usage to manage costs, and correlate GPU performance with other critical components of their AI stack. The integration is available for version 7.47+ of the Datadog Agent and includes templates to help configure both the Agent and the Exporter to collect critical metrics from environments.

Datadog has announced several new features and integrations at DASH 2023, including LLM-powered observability, Bits AI, Flex Logs, Live Search for Log Management, Reference Tables, and more. These additions aim to provide deeper visibility into AI ecosystems, improve troubleshooting with LLM Observability, enhance DevOps copilot capabilities with Bits AI, and streamline log management with Flex Logs and Live Search. Additionally, Datadog has expanded its APM features with Single-Step Instrumentation, Trace Queries, and Reproduce Exceptions, as well as introduced new products for security, cloud cost optimization, serverless monitoring, database monitoring, and more. These updates are designed to help users gain better visibility into their environments, improve troubleshooting and debugging capabilities, and optimize costs and performance.

Datadog Mobile Application Testing delivers fast, no-code, and reliable mobile app testing on real devices in the cloud, enabling any team member to create and maintain automated tests that seamlessly integrate into CI/CD pipelines. The platform allows users to record tests just one time and replay them across different devices on the same operating system, providing a realistic representation of key application workflows with screenshots and detailed pass/fail results. It also supports Assertions, Subtests, local and global variables, and manual recording of steps, making it easy for non-technical users to create and run tests without code. The platform provides self-healing tests, robust scheduling, and monitoring, allowing teams to ship mobile apps with confidence and increase release velocity.

Datadog has launched its Ambassadors program to recognize and highlight exceptional contributors who share their expertise, unique solutions, and experiences with the platform. These Datadog Ambassadors are technical experts who also serve as community leaders, fostering a welcoming environment for others. The program aims to be inclusive, helpful, and rewarding, offering various benefits such as exclusive opportunities, free certification exams, and limited-edition swag. A diverse group of inaugural ambassadors has been introduced, including Xing Du, Mike Stemle, Kristina Kondrashevich, Benjamen Pyle, Suraj Tikoo, Divya Gupta Arora, Martin Amps, and Willian Valerio, who have demonstrated their dedication to the Datadog community through various contributions such as blog posts, conference talks, open-source code, and more.

Datadog has introduced Quality Gates, a feature designed to enhance code quality management as organizations grow, by automatically blocking code merges that fail to meet predefined quality standards. This tool allows engineering teams to set specific rules to maintain code quality, performance, and security, ensuring that problematic elements like flaky tests and reduced code coverage do not reach production. Quality Gates also supports Static Analysis to enforce coding best practices and address issues such as security vulnerabilities and maintainability concerns before deployment. By allowing flexible configuration of rules, Datadog provides a comprehensive approach to maintaining high standards in software development, while enabling rapid feature development and testing. Now available in preview, Quality Gates can be tailored to individual organizational needs, ensuring that code adheres to specific quality metrics without impeding development speed.

The text discusses the challenges faced by mobile application teams, including engineers, designers, and product managers, in meeting user expectations for fast, seamless app experiences. It introduces Datadog's Mobile Session Replay, which now supports multiple languages and frameworks to help developers understand user interactions and troubleshoot issues more effectively. The tool provides a visual representation of user engagement, which allows developers to recreate bugs and identify the root causes of problems without compromising user privacy. By integrating with APM and RUM, Mobile Session Replay offers a comprehensive view of application performance and user behavior, aiding in the development of user-friendly, high-performing applications. The text also highlights the tool's role in improving app features and addressing customer frustrations, ultimately enhancing the user experience and potentially increasing revenue.

The text discusses the integration of artificial intelligence (AI) into applications using large language models (LLMs) and highlights the importance of optimizing monitoring systems to manage AI tech stacks effectively. Datadog offers comprehensive solutions for monitoring various components of AI systems, including infrastructure, data storage, model serving, and deployment. It provides out-of-the-box dashboards and detailed metrics for tools like NVIDIA DCGM Exporter, CoreWeave, Ray, and Slurm, among others. Additionally, Datadog facilitates the monitoring of vector databases like Weaviate and Pinecone, data integration engines like Airbyte, and applications built using frameworks such as PyTorch and NVIDIA Triton Inference Server. Other integrations include popular AI platforms like Vertex AI, Amazon SageMaker, and services like LangChain and Amazon CodeWhisperer, allowing seamless monitoring of AI models from providers like OpenAI, Google Gemini, and others. The text emphasizes the need for a flexible monitoring strategy to prevent operational challenges as AI technologies evolve and Datadog's role in providing visibility across the AI stack to optimize performance and manage costs.

The text introduces Flex Logs, a new feature by Datadog designed to streamline log management by centralizing logs on a single platform, regardless of the use case. This innovation builds on Datadog's Logging Without Limits™ by separating log ingest from storage and query costs, offering both short- and long-term log retention without compromising visibility. Flex Logs addresses challenges in managing exponentially growing log volumes from diverse sources by allowing organizations to retain logs at a cost-effective rate while still being able to query them rapidly. It provides tailored solutions for different log types, such as using standard indexing for frequently queried application logs and archiving for long-term compliance logs. For high-volume logs like security or network logs, Flex Logs offers a middle ground, ensuring logs are quickly accessible when needed without exorbitant costs. This enables enhanced observability and efficient log management, eliminating the overhead of complex queries and additional tools while integrating seamlessly with other Datadog data like metrics and traces.

Datadog has released an updated UX on its Network Performance Monitoring (NPM) Overview page to simplify network investigations and enhance visibility into network components. The new story-centric UX organizes traffic data into distinct categories, enabling users to quickly grasp key network information within various problem-solving use cases. Additionally, the NPM Analytics and DNS pages have been updated with recommended queries to jumpstart network investigations. This update helps identify top traffic costs, understand service dependencies, gain insights into DNS health, and utilize preloaded queries for troubleshooting network performance issues.

Datadog has released an updated user interface (UX) for its Network Performance Monitoring (NPM) feature, designed to simplify network investigations and provide guidance in identifying key issues such as top traffic costs, service dependencies, DNS health, and areas where recommended queries can help jumpstart investigations. The new UX automatically organizes traffic data into distinct categories, enabling users to quickly grasp key network information within the context of various problem-solving use cases. Additionally, Datadog has embedded recommended queries on the NPM Analytics and DNS pages to preload critical metrics for troubleshooting network performance issues.