February 2025 Summaries

Datadog RUM's Unity SDK is designed to help mobile game developers optimize gameplay, improve stability, and maintain player retention by proactively addressing performance issues in real-time. The SDK captures key events such as crash reports, network requests, and scene transitions, providing actionable insights into performance data. By setting up the SDK, developers can quickly identify and troubleshoot common performance issues, including crashes, latency, poor frame rates, and long load times, and take mitigating actions to maintain player satisfaction and boost engagement. The platform offers seamless integration with Unity's workflow, real-time monitoring, visualizations, alerts, and insights, enabling developers to make data-driven decisions and improve their game's overall performance.

Datadog has introduced an Attacker Clustering feature to help organizations detect and respond to distributed attacks. This new feature groups attacker behaviors into distinct clusters, enabling the identification of complex threats and providing a holistic view of attacker strategies. By analyzing patterns from attributes such as user agents or Datadog Attacker fingerprints, the system can accurately detect stealthy threats while filtering out normal traffic. The clustering mechanism tracks evolving attacker strategies, adapts to new attack behaviors, and provides clear insights for incident response teams to quickly identify and respond to coordinated attacks.

Datadog has launched new integrations with Supabase, DuckDB, and Milvus to enhance visibility and performance monitoring in modern data platforms. These integrations cater to different aspects of data management, including serverless data workflows with Supabase, in-process analytics with DuckDB, and vector search for AI/ML with Milvus. By consolidating metrics, logs, and traces, Datadog enables teams to efficiently diagnose issues, optimize resource allocation, and ensure high performance across their infrastructure. The integration with Supabase aids developers in managing query behavior and resource contention, DuckDB's integration focuses on maximizing resource efficiency and performance, while Milvus integration allows for monitoring AI workloads, ensuring responsive and efficient vector-based search capabilities. Overall, these integrations help reduce monitoring complexity and mitigate risks, supporting the development of high-performing applications.

Datadog has introduced three new integrations for modern database platforms—Supabase, DuckDB, and Milvus—to enhance real-time analytics and AI/ML capabilities by offering comprehensive visibility and performance monitoring. These integrations help streamline database operations by consolidating workflows and processes, allowing users to efficiently diagnose issues and optimize resource allocation. The Supabase integration aids in monitoring serverless PostgreSQL database workflows; DuckDB integration focuses on in-process analytics, optimizing performance and resource efficiency; and the Milvus integration supports vector searches for AI/ML workloads. By consolidating metrics, logs, and traces, Datadog reduces the complexity of monitoring modern data stacks and enhances the delivery of high-performing applications.

Observability Pipelines from Datadog has integrated with platforms like SentinelOne Singularity Data Lake, enabling the collection and processing of security logs in a cost-effective manner. This integration allows users to collect various types of EDR logs, including activity logs, threat logs, alert logs, file and registry change logs, and aggregate them into a standard format for routing to SentinelOne. The platform also provides features such as parsing, standardizing, enriching, filtering, deduping, and sampling logs to control log volumes, identify threats in real-time, and generate metrics to reduce the volume of logs sent to SentinelOne. With Observability Pipelines, users can centralize log processing, avoid vendor lock-in, and start routing data to SentinelOne Singularity Data Lake by setting up the destination and environment variables.

DASH 2025 will be held in New York City at the Javits Center from June 9 to 11, offering a three-day learning and community experience for thousands of Datadog users. The event features expert-led sessions, hands-on experiences, certification opportunities, and networking with peers. A new day has been added to the schedule, featuring workshops and the Partner Summit, which will include a keynote presentation, partner awards, and a networking reception. Keynote presentations will highlight Datadog's product updates and latest releases, while breakout sessions and expo theaters will provide in-depth learning on various products and customer success stories. Hands-on learning workshops will be available across all three days, covering topics such as cloud platforms and Kubernetes. The event also includes the opportunity to earn prizes in gamified experiences and take a Datadog certification exam in person. Super early bird pricing is available until March 31, with a 14-day free trial option for new users.

When Google Cloud Run abstracts away infrastructure management and runs on complex backends, it can be difficult to troubleshoot. Datadog's integrations with Google Cloud and Google Cloud Run address this challenge by collecting and visualizing key metrics and logs. The Datadog Agent sidecar enables you to instrument your application and set up monitoring directly from the Cloud Run UI, removing the need for code changes. With the sidecar, you can collect custom metrics, traces, and logs in real-time, providing a seamless, centralized monitoring experience. By setting up the sidecar directly in Google Cloud, you can simplify integration, reduce setup time, and avoid configuration risks. The Datadog Serverless view allows you to visualize your Cloud Run telemetry, investigate issues, and identify bottlenecks within a single view.

Synthetic tests in modern web applications often face challenges due to frequent UI updates and changes. To address this, locators are used to uniquely identify elements across different design iterations. CSS and xPath selectors are two common methods for creating locators, with CSS being easier to learn and read but offering fewer advanced querying options, while xPath provides more flexibility but requires a higher learning curve. Datadog Synthetic Monitoring offers a self-healing locator system that automatically covers most use cases, but also allows for customization using testing templates and user-created locators to address specific edge cases or intentional test failures. By implementing locators in synthetic tests, developers can ensure their applications remain resilient to UI changes and rendering issues.

Datadog's Database Monitoring (DBM) Recommendations aim to proactively optimize database performance by analyzing explain plans and query metrics. DBM identifies indexing opportunities, prioritizes recommendations based on severity, and provides contextual workflows for evaluation and implementation. The tool draws from a wide range of data sources, including static data such as explain plans and table schemas, as well as dynamic sources like query metrics. By applying this holistic approach, DBM helps users optimize database performance with confidence and stay ahead of potential issues.

Datadog Observability Pipelines helps organizations manage the volume and variety of logs generated by critical AWS services such as VPC Flow Logs, AWS WAF, and Amazon CloudFront. By integrating these services with Observability Pipelines, teams can aggregate and process logs from various sources including Amazon S3, Amazon Data Firehose, and AWS Lambda, extract actionable insights from AWS WAF, CloudFront, and VPC Flow Logs, and gain more value from their AWS logs without vendor lock-in. The solution provides a centralized control plane to set up, manage, and optimize log flows, allowing teams to apply transformations, filter repetitive events, sample high-volume logs, and send logs to preferred destinations such as Datadog Log Management or SIEMs for security analytics.

Guillaume Bort from Datadog shares their experience of scaling Apache Kafka to meet the demands of a massive data platform. The company built a custom Streaming Platform to abstract Kafka's complexity, enabling real-time reliability at scale. This platform uses Streams to build resilient pipelines decoupled from specific clusters, an Assigner for dynamic cluster management, and a smarter commit log to overcome traditional Kafka limitations such as head-of-line blocking. A custom client library called libstreaming was developed in Rust to optimize performance and observability across all applications. The Streaming Platform allows Datadog to treat Kafka infrastructure like commodity hardware, modulating workloads across clusters, automatically replacing unhealthy components, and ensuring uninterrupted data flow.

The Datadog Certification Program is designed to help technical professionals stand out in the industry by providing a path to build and demonstrate their knowledge of Datadog's platform and best practices. The program offers three certifications: Fundamentals, Log Management, and APM, and allows partners to showcase their competency to potential customers. To prepare for the exams, candidates can utilize free resources at the Datadog Learning Center, including conceptual and hands-on materials, practice exams, and exam guides. Exams can be purchased individually or in bulk for companies, with a virtual option available as well as over 1,000 test centers worldwide. Upon successful completion, candidates will receive a badge that can be included on their resume or social media profiles.

The Software Catalog has evolved to provide teams with an improved framework for modeling their software environments. This new tool helps eliminate knowledge gaps and silos by making it possible for teams to easily view, discover, and understand the many interrelated components that make up their organization's software ecosystem. With features such as extended entity types, manual relationship definition, multi-ownership, custom filters for logs and events, automatic validation through IDE plugins, self-service actions, and a comprehensive and customizable view of the software environment, Software Catalog empowers teams to spot and address problems in their environments more quickly and accurately. This evolution is designed to help organizations manage the complexities of their software landscapes, improve collaboration across teams, and ensure that security and reliability standards are upheld throughout their organizations.

The text outlines how Datadog's integrations with Atlassian Jira and Confluence help organizations manage and secure their collaboration platforms by centralizing audit logs and event data. These integrations facilitate the collection and analysis of logs related to account management, system configuration, and product access, allowing security teams to visualize data, generate metrics, and receive alerts through a centralized platform. By using Datadog Cloud SIEM, automatic detection of suspicious activities and enhanced visibility into security posture are possible, with built-in rules tailored to detect threats such as unauthorized access and privilege escalation. Preconfigured dashboards offer real-time insights and support long-term investigations by tracking sensitive activities and potential breaches. This comprehensive approach enables organizations to maintain a robust security stance and efficiently respond to evolving threats.

Datadog Observability Pipelines integrates seamlessly with Microsoft Sentinel, enabling security teams to collect, transform, and route logs to this SIEM solution without requiring custom scripts or preprocessing. This integration simplifies the SIEM migration process by centralizing log collection and ETL functions within the infrastructure, routing logs according to network policies. With Observability Pipelines, security teams can standardize log processing, enrich logs with GeoIP information, remap security logs from various sources to a standardized format, handle unstructured logs, and route data to Microsoft Sentinel for threat detection and native incident response capabilities. This integration also enables security teams to prioritize and route logs to Microsoft Sentinel alongside other destinations without losing visibility or sacrificing compliance, simplifying the SIEM migration process and reducing costs associated with managing log splitting across multiple vendors.

The Datadog Monitor Status page is designed to enhance the effectiveness of engineering teams by providing a centralized hub for accessing rich contextual information related to monitor alerts. This tool addresses the challenges posed by the complexity of modern, distributed applications, where breaking down knowledge silos is crucial for efficient incident response and minimizing service disruptions. It offers a comprehensive view of monitor behavior, configuration, and historical context, enabling teams to quickly orient investigations, identify trends, and correlate alerts with recent changes such as deployments or configuration updates. The page also integrates features like an Events Timeline and Dependency Map to streamline troubleshooting, helping teams assess the impact of issues and identify root causes. By offering actionable insights and in-depth guidance, the Monitor Status page aims to facilitate a more effective and cohesive approach to incident management.

Insurance companies face significant challenges in managing and securing sensitive data due to the complexity of the data types, fragmented storage across multiple departments and legacy systems, and reliance on third-party SaaS solutions. To address these challenges, Datadog's Sensitive Data Scanner (SDS) can be used to discover, classify, and redact sensitive information in telemetry data, including logs, APM spans, and RUM events. SDS also enables the automatic flagging of instances of PII stored in Amazon S3 buckets and RDS instances as new resources are spun up. Additionally, SDS helps triage and remediate sensitive data vulnerabilities by providing a centralized view of security posture and identifying potential vulnerabilities for cloud storage environments. By using SDS, insurance companies can simplify the discovery, classification, and management of sensitive data across their telemetry data and cloud storage environments, helping to mitigate risk and comply with regulations such as HIPAA, GDPR, and CCPA.

Datadog's Google Cloud integration provides a centralized view into Google Cloud Tensor Processing Units (TPUs) utilization, performance, and resource consumption. This enables organizations to quickly visualize TPU metrics with an out-of-the-box dashboard to optimize performance, be alerted to possible rightsizing opportunities using recommended monitors, and gain insights into their TPU usage across virtual machines, GKE, custom job runners, and more. The integration provides visibility into TensorCore utilization, duty cycle metrics, memory usage, and other performance metrics, helping organizations detect resource bottlenecks, underutilization, and optimize cloud spend while fine-tuning batch configurations to maximize hardware resource utilization.