June 2022 Summaries

Datadog's Database List provides real-time insights into the performance of all your databases, allowing users to identify and troubleshoot overloaded hosts and investigate problematic queries throughout their infrastructure. The list includes active connection information and in-depth wait event analysis for understanding query performance. Users can also set up monitors to receive alerts when queries exceed a certain time limit. This comprehensive monitoring solution helps users optimize their database performance and prevent future issues.

In this month's Datadog Spotlight Series, Sean Schaab, Sales Engineering Team Lead based in Los Angeles, shares their journey to Datadog and the personal and professional growth they have experienced. Sean discusses how they overcame challenges related to gender identity and embraced vulnerability as an essential part of growth. They also highlight the importance of being oneself at work and creating a supportive, empathetic, and vulnerable team culture. Additionally, Sean talks about their involvement in Rainbowdogs, Datadog's LBGTQIA2S+ Community Guild, and how it has helped them feel seen and connected to others at work. They encourage others to join the growing company and experience #DatadogLife.

Sean Schaab, a Sales Engineering Team Lead at Datadog, has experienced personal and professional growth in his role. He initially joined the company as a Solutions Engineer after completing a bootcamp in web development, but struggled with feeling disconnected from himself due to his gender identity. After accepting himself and coming out at work, he became an advocate for LGBTQIA2S+ inclusion within the company, supporting initiatives such as adding pronouns to Slack profiles and Zoom. Sean's leadership style emphasizes empathy, vulnerability, and trust-building, which has helped him thrive in his role and create a supportive environment for his team. He is proud to be part of the Rainbowdogs Community Guild, an LGBTQIA2S+ community guild within Datadog, where he has made lifelong friends and feels seen.

The Datadog Database List is a tool that allows users to monitor their entire database fleet in one place, providing real-time insights into database performance and identifying issues such as overloaded hosts and problematic queries. By analyzing wait event information, users can understand where queries are spending most of their time and investigate slowdowns by correlating changes in query performance with infrastructure metrics, logs, and database telemetry. The Database List also provides detailed query metrics, including top queries, user execution times, and query durations, enabling users to troubleshoot issues and optimize their code. Users can filter databases by source, team, cluster, and custom tags, and set up monitors to automatically alert them when queries exceed a certain time limit, allowing for quick analysis and investigation of performance issues.

Datadog has introduced a new feature called Frustration Signals as part of its Real User Monitoring (RUM) to help businesses identify and mitigate user experience issues that can lead to customer frustration and potential site abandonment. This tool detects patterns such as rage clicks, dead clicks, and error clicks, which indicate user annoyance due to design flaws or functional errors. By using an out-of-the-box dashboard, companies can visualize these frustration signals, focusing troubleshooting efforts on the most problematic areas, and proactively address issues before they result in customer churn. The RUM Explorer further facilitates this process by allowing for in-depth analysis of user sessions, helping businesses to prioritize resolving issues on pages critical to business operations, such as those involving checkouts and signups. Session replays and detailed action waterfalls provided by the tool enable developers to comprehend and address user frustrations effectively, enhancing overall user satisfaction and experience.

Amazon Web Services (AWS) has introduced its third generation of custom Arm-powered processors, Graviton3 EC2 instances, which promise up to 25% better performance for compute-intensive workloads compared to Graviton2. These instances can be beneficial for applications like distributed data analytics, machine learning, video encoding, gaming, and more, as they offer improved performance, cost savings, and energy efficiency. Datadog provides full visibility into AWS environments, including any Graviton3-powered instances, allowing users to compare performance between architectures and confirm that their applications perform well when moving to these new instance types. The Datadog Agent fully supports monitoring both x86 and Arm-based hosts, providing granular insights into EC2-hosted workloads. Additionally, Datadog APM enables the collection and analysis of trace data from services running on different instance types for direct comparison of request latency and other telemetry. AWS Graviton Ready designation ensures that Datadog fully supports monitoring all Graviton-powered workloads no matter where they are hosted, including EC2 instances, AWS Lambda, or AWS Fargate.

AWS's new Graviton3 EC2 instances are built on its third generation of custom Arm-powered processors, promising up to 25 percent better performance over Graviton2 for compute-intensive workloads. This means that migrating to Graviton3 instances can provide better performance, cost savings, and more energy efficiency. Datadog provides full visibility into your entire AWS environment, including any Graviton3-powered instances you are running workloads on, enabling you to directly compare performance between architectures and confirm that your application performs well when moving to these new instance types. Once enabled, Datadog will automatically collect telemetry from across all EC2 instances, including Graviton3-powered hosts, without additional configuration. The Datadog Agent fully supports monitoring both x86 and Arm-based hosts, providing granular insights into your EC2-hosted workloads. Having visibility into the performance of similar workloads across different EC2 instance types helps determine whether your application is showing sufficient performance improvements after migrating to Graviton3. Additionally, Datadog has achieved AWS Graviton Ready designation and fully supports monitoring all Graviton-powered workloads on any platform, including serverless environments via AWS Lambda or AWS Fargate.

Azure App Service is a platform-as-a-service (PaaS) offering that allows users to deploy applications without worrying about infrastructure management. It uses a "serverless" approach, which eliminates the need for provisioning or managing servers. However, understanding the relationships between App Service resources can impact performance and costs. Datadog has released support for Azure App Service in its Serverless view, allowing users to visualize all their Azure App Service resources, understand relationships between apps and App Service plans, quickly spot issues, identify underutilized and overloaded App Service plans, and gain insight into APM usage.

Log management can be challenging when dealing with old logs stored in cold storage environments. Retrieving and querying these archived logs is often slow, expensive, and work intensive. Datadog's Log Rehydration™ feature allows users to efficiently archive all of their logs and retrieve the exact ones needed for analysis at any time. This solution enables fast scanning and reindexing of terabytes of archived logs within hours, making it easier to access information for troubleshooting and root cause analysis. By organizing old logs into separate archives based on frequency of use, users can optimize storage costs and improve efficiency in searching for relevant log data.

Datadog, a rapidly growing SaaS company, has announced the opening of a new engineering hub in Lisbon, Portugal. The idea for this hub came from Nuno Antunes, VP of Engineering for the Datadog Cloud Security Platform, who recognized Lisbon's diverse and competitive talent pool as ideal for expanding their product suite. The Lisbon hub is set to be fully operational by July 2022, with plans to grow tenfold by year-end. Datadog aims to leverage Lisbon's tech professionals to develop new features that will enhance its platform and customer experience.

The Internet of Things (IoT) involves various devices such as autonomous vehicles, automobiles, planes, electric charging stations, and voice controllers. These devices are embedded with gateways, electronics, actuators, platform hubs, and cloud-service connectivity to exchange data across the physical, network, and application layers of IoT architecture. IoT devices typically emit high-cardinality data, which can be efficiently captured using logs. Datadog is a log management solution that helps organizations monitor IoT devices in real time and at scale by offering tools for tagging, querying, and visualizing high-cardinality log data. It addresses challenges such as collecting and storing globally distributed IoT data, investigating high-cardinality data from IoT fleets, and correlating business-level KPIs with device and system metrics. Datadog's Logging without Limits™ enables cost-effective indexing of relevant or high-priority data for long-term storage and analysis.

Sara Verdi from Datadog is thrilled to announce the opening of a brand new engineering hub in Lisbon, Portugal, marking an exciting expansion for the company. The hub will be led by Nuno Antunes, VP of Engineering for the Datadog Cloud Security Platform, who recognized Lisbon's diverse and competitive talent pool as the perfect location for this new venture. As the local leader, Antunes has been working to establish fair office practices and comprehensive benefits packages while hiring a team of engineers focused on growing observability and security products. With the hub fully operational in July 2022, Datadog is eager to collaborate with Lisbon's tech professionals to build new features that enhance its platform and customer experience.

Datadog is designed to help organizations address the challenges of monitoring IoT devices at scale. The company's Logging without Limits solution can capture high-cardinality data from connected devices, enabling real-time visibility into device performance and security issues. Datadog's IoT Agent continuously streams metrics and logs from devices, eliminating gaps in visibility caused by connectivity or storage capacity issues. The platform offers tools for enriching and analyzing high-volume data, ensuring the availability of IoT environments with automated monitors, and expediting troubleshooting of unusual device activity with Log Anomaly Detection. Additionally, Datadog enables users to correlate business-level KPIs with device and system metrics in a comprehensive dashboard. By leveraging these features, organizations can gain deeper insights into their IoT devices and improve monitoring efficiency and costs.

Datadog Database Monitoring (DBM) offers comprehensive visibility into SQL queries running on databases such as self-hosted SQL Server, Azure SQL Database, and Azure SQL Managed Instance. DBM helps troubleshoot database performance issues by analyzing historical trends in query metrics and execution plans. It also provides deep insights into managed PostgreSQL and MySQL databases on Azure, enabling users to build and scale workloads with confidence. With DBM, users can monitor the health and performance of SQL Server and Azure Database for PostgreSQL and MySQL, detect performance issues, optimize query performance, and visualize resource metrics.

DBM provides comprehensive visibility into SQL queries running on databases, allowing users to troubleshoot performance issues by analyzing historical trends in query metrics and execution plans. For SQL Server, DBM offers optimization of query performance through the identification of slow queries, plan regression, and memory grant usage, enabling users to reduce latency and prevent database pressure. In addition, DBM provides full visibility into Azure managed databases for MySQL and PostgreSQL, allowing users to build and scale workloads with confidence. With expanded ecosystem support, DBM gives users around-the-clock visibility into their databases' health and performance, regardless of whether they are using Azure's managed offerings or self-hosted databases.

Azure Kubernetes Service (AKS) users can now benefit from an out-of-the-box dashboard by Datadog that provides immediate visualization of the health and performance of their AKS clusters. The new dashboard organizes critical information from standard AKS metrics while incorporating log data for observability into the control plane. This update enables automatic processing and visualization of Azure resource logs, providing insights into the control plane without manual configuration. Datadog's AKS integration also allows users to monitor important resource utilization metrics like CPU, memory, and storage usage, as well as cluster health information such as pod phase and state. The dashboard provides visibility at the cluster, node, and pod levels, enabling users to troubleshoot orchestration issues and gain better insights into their AKS clusters.

Datadog has released an out-of-the-box Azure Kubernetes Service (AKS) dashboard that allows users to visualize the health and performance of their AKS clusters immediately. The dashboard organizes and highlights critical information from standard AKS metrics, incorporating log data to provide observability into the control plane. This update enables users to monitor cluster health and performance at multiple levels, including pods, nodes, and clusters, with no manual configuration required. The new dashboard provides visibility into AKS control plane components, enabling users to troubleshoot orchestration issues, diagnose potential workload problems, and gain better insights into their containerized applications. To get started, users can install the Datadog Azure integration and configure log forwarding, or deploy the Datadog Agent into their AKS cluster for even deeper visibility.

Datadog's Network Device Monitoring (NDM) has expanded its capabilities by collecting Simple Network Management Protocol (SNMP) Traps to provide comprehensive visibility into network equipment health and performance. This feature enables network engineers to catch critical network issues in real-time, consolidate troubleshooting efforts within a single platform, and set up monitors for SNMP Trap events to receive notifications for potential issues before they impact the rest of the network. By leveraging alerts on SNMP Trap data alongside various network metrics, users can diagnose issues, assess their severity, and immediately start troubleshooting.

Datadog's Network Device Monitoring (NDM) now collects SNMP Traps to provide real-time insights into network equipment, enabling the capture of critical issues as they happen. This feature expands on existing NDM capabilities, allowing for the consolidation of troubleshooting efforts within a single pane of glass. With SNMP Trap support, users can identify device issues promptly and troubleshoot them using detailed device metrics, such as packet drops and interface saturation. The integration enables users to receive alerts via email, ticketing tools, or mobile devices, streamlining device monitoring and improving overall network visibility.

Gartner® has recognized Datadog as a "Leader" in its 2022 Magic Quadrant™ for Application Performance Monitoring (APM) and Observability, with the highest position for Ability to Execute. This recognition is attributed to customer feedback shaping Datadog's products and services. The company's unified approach to APM and observability helps break down organizational silos and provides a fully integrated experience that meets customer needs across IT operations, security, and development teams. Key differentiators include Watchdog, an AI engine for monitoring cloud-native architectures, and Real User Monitoring (RUM). Datadog's recognition is made possible by the engagement and feedback from its customers worldwide.

Datadog has been recognized as a Leader within the Magic Quadrant for APM and Observability by Gartner, securing the highest position for Ability to Execute. This recognition is attributed to customer feedback, which plays a crucial role in shaping Datadog's products and services to address complex challenges in cloud and hybrid environments. Datadog's unified approach to APM and observability breaks down organizational silos, creating a fully integrated experience that meets customer needs. The company's customers have identified key differentiators such as Watchdog's AI engine and Real User Monitoring (RUM) as essential components of their workflow. Datadog attributes its recognition to the engagement and feedback from its global customer base, which has helped improve the platform and advance its mission.

Datadog Audit Trail provides granular, centralized records of user and API activity throughout the platform, allowing stakeholders in leadership roles to maintain compliance, enforce governance, and build transparency. It helps organizations meet crucial privacy and reporting standards like HIPAA, GDPR, FedRAMP, and CCPA by providing visibility into user actions and system events. Audit Trail can be used alongside other Datadog services for greater control over teams' usage of the platform.

Datadog Audit Trail provides granular, centralized records of user and API activity throughout the Datadog platform, allowing teams to maintain compliance, enforce governance, and build transparency. It enables stakeholders in leadership roles to spot gaps in enablement, budgeting, and reporting, as well as build a modern compliance strategy for their organization. Administrators and security analysts can use Audit Trail to ensure operational workflows stay intact and follow up on incidents. The tool provides end-to-end visibility into user actions, helping organizations meet crucial privacy and reporting standards like HIPAA, GDPR, FedRAMP, and CCPA. It also allows users to visualize day-to-day user activity, set up alerts on system events, and identify malicious users in the case of a security breach. By using Audit Trail alongside Role Based Access Control (RBAC) and Sensitive Data Scanner, organizations can proactively manage access and retrospectively investigate and diagnose security issues that affect compliance. The tool enables teams to ensure Datadog platform governance across teams, investigate and diagnose issues caused by configuration changes, and increase transparency using context-rich insights.

Volexity identified a critical vulnerability in Atlassian Confluence Server and Data Center that allowed attackers to launch remote code execution (RCE) exploits, which were actively exploited as early as May 2022. The exploit takes advantage of an Object-Graph Navigation Language (OGNL) injection vulnerability in the Confluence Server, allowing attackers to introduce security flaws to applications and frameworks that use it. Atlassian released a security advisory to address the unauthenticated RCE vulnerability, stating that this attack affected all supported Confluence Server and Data Center products. Remediation involves upgrading to versions equal to or greater than 7.4.17, 7.13.7, 7.14.3, 7.15.2, 7.16.4, 7.17.4, and 7.18.1, or applying Atlassian's recommended workaround to mitigate the risk of an exploit. Datadog Security Research confirmed active exploitation of this vulnerability from information-sharing partners as early as May 2022.

Tay Nishimura, an infrastructure engineer on the Distributed Caching team at Datadog, shares her journey through the tech industry. Starting as a mathematician and visual thinker, she transitioned to computer science with internships at Amazon and Google. However, she struggled with adapting to the practical demands of software development and feeling like an outsider in the industry. After switching jobs and realizing her passion for site reliability engineering (SRE), Tay pursued this path despite initial doubts about her experience. She developed ToyNet, an open-source learning platform with network emulation, which helped prove her capabilities to potential employers. At Datadog, Tay's visual thinking style became a major asset in understanding complex systems and communicating effectively with her team. Throughout her career, she found support among other women in tech who faced similar communication and expectation challenges. Today, Tay is a member of the caching platform team at Datadog and continues to contribute to Project Reclass, a nonprofit vocational program aimed at teaching technical skills to the incarcerated and military veterans.

Tay Nishimura, an infrastructure engineer at Datadog, reflects on her journey through the tech industry. She transitioned from a software engineering role to site reliability engineering and later found fulfillment in product management. Her visual thinking style, which she initially struggled with in coding, became a valuable asset when she began drawing diagrams to understand complex systems. Tay's experiences highlight the importance of finding one's niche in the vast tech industry, where guidance on career paths is often limited. She emphasizes the need for women and underrepresented groups to find supportive communities and spaces within the industry, where they can feel valued and understood.

Modern video streaming workflows involve multiple services like encoders, origins, ad servers, and CDNs, which can make observability challenging due to inconsistent and limited log data. To address this, Datazoom collects real-time data from different streaming components and standardizes it into JSON, integrating with Datadog to allow visualization, analysis, and real-time alerts. This integration offers a centralized dashboard displaying key Quality of Experience metrics such as Time to First Frame and Rebuffer Ratios, which help identify playback issues affecting customer satisfaction. The dashboard can be customized to include telemetry from other infrastructure components, like Amazon DynamoDB, and allows for dynamic filtering using template variables. By correlating playback data with upstream service performance, such as CDN metrics, users can pinpoint causes of streaming issues and optimize settings to improve cache efficiency. Additionally, standardized logs enriched with contextual tags from Datazoom provide deeper insights into video workflow events, aiding in efficient troubleshooting. Datadog supports monitoring Datazoom telemetry alongside hundreds of other services, offering a comprehensive view of video workflows to enhance user experience.