January 2022 Summaries

Microsoft Azure Storage is a cloud-based service that provides highly available, geographically redundant storage for on-premise and cloud applications. It offers client libraries for various languages such as PHP, Java, Python, and Ruby. Datadog has added Azure Storage to its list of Microsoft Azure cloud services that can be monitored. Azure Storage includes four services: Blob storage (for unstructured data), Table storage (for structured NoSQL data), Queue storage (for message delivery buffering), and File storage (accessible via the SMB protocol). It offers several data redundancy options, including locally redundant storage, zone-redundant storage, and geo-redundant storage. Datadog's Azure Storage integration provides access to various metrics that can be graphed, alerted on, or visualized in out-of-the-box dashboards. The integration also automatically pulls in resource metadata tags for filtering and creating targeted alerts.

On January 25, 2022, Qualys announced a critical local privilege escalation vulnerability in PolicyKit's pkexec, known as PwnKit, impacting multiple major Linux distributions like Ubuntu, Debian, Fedora, and CentOS. This vulnerability, with a CVSS score of 7.8, enables attackers to gain root access by exploiting the PolicyKit executable through specially crafted environment variables that trigger the loading of an arbitrary library file. Despite its widespread presence, major Linux distributions have issued patches to mitigate the risk. Datadog's Cloud Workload Security offers real-time monitoring and detection for this vulnerability, emphasizing the importance of a defense-in-depth security strategy. The vulnerability highlights the necessity for organizations to maintain updated systems and consider using minimal Linux distributions for containerized workloads to reduce attack surfaces.

Stratus Red Team is an open-source project designed to emulate attack techniques in cloud environments, particularly focusing on AWS, to validate threat detection systems. It is a lightweight Go binary available on GitHub, offering AWS-specific attack simulations such as credential access, discovery, defense evasion, and exfiltration, all aligned with the MITRE ATT&CK framework. The tool facilitates the full lifecycle of attack techniques, including creating necessary infrastructure, executing attacks, and cleaning up afterward. Users can interact with Stratus Red Team through a command-line interface, and it can also be integrated programmatically as a Go library for automation purposes. The project plans to expand its support to Kubernetes and Azure and will continue to evolve with community feedback.

Content delivery networks (CDNs) play a crucial role in reducing latency by delivering cached data from a network of proxy servers, thus enhancing the end-user experience by lowering the load on origin servers and shortening data travel time. Datadog's Synthetic Monitoring now provides visibility into which CDN providers are loading resources during Synthetic tests, along with their cache status, allowing users to identify outdated caches that may cause testing issues and optimize CDN coverage accordingly. This feature is particularly useful for troubleshooting slow tests, as it helps pinpoint latency sources, such as CDN misses due to expired caches, and enables users to adjust cache policies for improved performance. Datadog integrates with various CDN providers like Akamai, Cloudflare, and Amazon CloudFront, offering insights into their usage within HTTP-based Synthetic tests to further enhance user experience. This visibility allows for quick identification of problematic CDNs, facilitating efficient support contact and configuration adjustments, and is offered to Datadog users through a 14-day free trial for those who are new to Synthetic Monitoring.

Government agencies are increasingly moving their operations to the cloud and must adhere to strict compliance and security standards such as FedRAMP. Datadog has achieved FedRAMP Moderate Impact authorization, allowing public-sector organizations to use it for monitoring applications and infrastructure in the cloud. This unified solution integrates with major cloud providers like AWS, GCP, and Azure, providing full visibility into their Moderate Impact-level systems running in the cloud. Datadog helps agencies manage growing monitoring demands and maintain secure services.

Continuous integration (CI) is an essential approach in software development that allows organizations to iterate quickly while minimizing the risk of releasing faulty code. Jenkins, one of the most mature and widely used automation servers on the market, helps implement CI by enabling easy integration with other tools in the development workflow. Datadog Synthetic Monitoring offers a comprehensive suite of tests that can be run as part of Jenkins pipelines to gain visibility into applications during development. By adding these tests to existing Jenkins pipelines and monitoring their results in Datadog, developers can release software faster without sacrificing quality.

Amazon's AWS GovCloud (US) offers two isolated regions in its ecosystem specifically designed for US customers who meet strict security and compliance standards. Datadog has achieved Moderate-Impact authorization from the Federal Risk and Authorization Management Program (FedRAMP), allowing it to safely and securely monitor infrastructure and workloads running in AWS GovCloud (US) environments. Datadog provides complete visibility into your AWS GovCloud (US) environment, enabling you to monitor infrastructure and visualize key data such as host health, latency distribution, memory allocation, error rates, endpoint performance, and more. It integrates with over 650 unique technologies, allowing you to surface problems and find their correlating factors across the entirety of your stack. Datadog also offers full-stack AWS monitoring by integrating with the full suite of AWS services, including Amazon EC2, AWS Lambda, and Amazon S3. This makes it easy to gather business-critical metrics from your AWS GovCloud (US) environment into Datadog. Additionally, you can view logs and metrics from any services running in your stack using the Datadog Agent. Datadog simplifies inventory management by monitoring key infrastructure metrics on hosts, containers, processes, networks, and more, providing real-time insight into the overall posturing of your environment. It also helps manage cloud asset inventory by allowing you to tag host instances within your GovCloud environment according to criteria like OS and hardware version, Region, AMI ID, or Kubernetes cluster. Datadog provides unified visibility into your entire AWS environment, including services running in AWS GovCloud (US), with FedRAMP's Moderate-Impact authorization. Existing Datadog customers can set up their AWS GovCloud (US) integrations now, while new users can start a 14-day free trial.

Kai Xin Tai and Beth Glenfield discuss the importance of continuous integration (CI) in software development, highlighting its benefits of quick iteration and minimizing faulty code release risks. They also introduce Datadog Synthetic Monitoring as a tool to automate testing within CI processes, enabling organizations to detect issues before they reach production. The article guides readers on how to add Synthetic tests to existing Jenkins pipelines using the `@datadog/datadog-ci` NPM package, providing a simple-to-use interface for non-technical users. Once implemented, Datadog Synthetic Monitoring offers visibility into application performance and quality, allowing developers to release software faster without sacrificing quality.

Datadog has achieved FedRAMP Moderate Impact authorization, allowing public-sector organizations to use the platform to monitor and secure their cloud infrastructure. With this achievement, Datadog provides unified visibility into the public sector's cloud, integrating with major cloud providers like AWS, GCP, and Azure. This enables agencies to get full visibility into their Moderate Impact-level systems running in the cloud, monitoring health and performance across infrastructure, applications, networks, users' experiences, and more from a single platform. The platform provides features such as Synthetic Monitoring, cross-stack metric correlation tools, and integration with over 850 technologies, helping teams spot anomalies, identify root causes, and ensure service health, all while meeting strict compliance and security standards.

This article discusses the six pillars of AWS's Well-Architected Framework and how they can be applied to building secure, reliable, and high-performing serverless applications. The six pillars are security, reliability, performance efficiency, cost optimization, sustainability, and operational excellence. For each pillar, the article provides best practices that developers can follow to optimize their serverless applications. Additionally, the article highlights how a centralized monitoring platform like Datadog can provide full visibility into a serverless application's health and performance.

Serverless architecture is gaining popularity among organizations looking to modernize their applications due to its ability to increase agility and reduce operational overhead and costs. AWS Lambda functions, which are stateless and ephemeral by design, form the basis of serverless applications on AWS. This two-part series explores best practices for designing and building serverless applications on AWS, focusing on microservice design patterns that allow developers to create highly scalable and reliable applications. The first part discusses the shift from monoliths to microservices and outlines several well-established microservice design patterns, such as the Strangler pattern, State Machine pattern, Aggregator pattern, Publisher-Subscriber pattern, Circuit Breaker pattern, and Saga pattern. In the second part of this series, serverless best practices adhering to AWS's Well-Architected Framework will be examined.

Serverless applications on AWS offer increased agility while reducing operational overhead and costs. This paradigm is particularly well-suited for microservice-based architectures, which are gaining prevalence over monoliths due to their simplicity in development and deployment, but drawbacks such as tight coupling of components and full redeployment requirements. Serverless technologies like AWS Lambda functions can run small chunks of code in response to events emitted by other services, integrating with managed services like message queues, APIs, and event streams to minimize pain points associated with building microservices. A common design pattern for serverless microservices is the Strangler pattern, which gradually replaces components of a monolith with microservices using a strangler facade like API Gateway. This allows developers to migrate from monoliths to microservices while minimizing downtime and ensuring client requests are routed correctly. Managing complexity in microservice-based architectures can be achieved through patterns such as the State Machine pattern, which uses AWS Step Functions to orchestrate complex workflows, and the Aggregator pattern, which reduces chatty communication between clients and microservices by using a single Lambda function to accept all client requests and forward them to the appropriate services. Asynchronous and stream processing in microservices can be implemented through patterns like the Publisher-Subscriber pattern, which uses Amazon S3 to push messages to an SNS topic and triggers SQS queues to execute Lambda functions. Handling failures in distributed systems is crucial, and patterns such as the Circuit Breaker pattern, which monitors request failures and circuit breaker status using DynamoDB and a Lambda function, and the Saga pattern, which coordinates a sequence of local transactions in interconnected microservices using choreography or orchestration, can help ensure data consistency and fault tolerance. By adopting these design patterns and best practices, developers can build highly scalable and reliable serverless applications on AWS.

AWS's Well-Architected Framework is a set of best practices for designing and operating reliable, secure, high-performing serverless applications. The framework consists of six pillars: security, reliability, performance, cost optimization, sustainability, and operational excellence. To build secure serverless applications, developers can adopt best practices such as limiting Lambda privileges, implementing virtual private clouds, managing secrets and credentials, and using secret management services like AWS Secrets Manager. Reliability is achieved by ensuring high availability, managing failures, and optimizing function memory size. Performance optimization involves reducing cold starts, implementing caching, reducing initialization times, and optimizing function memory size. Cost optimization can be achieved by right-sizing Provisioned Concurrency and using Application Auto Scaling. Sustainability is ensured by defining and enforcing sustainability SLAs, running workloads in Availability Zones that use renewable energy, removing unused components, and scheduling jobs to prevent resource contention. Operational excellence is achieved through centralized monitoring with a platform like Datadog, which provides full visibility into the serverless application's health and performance.

Consul is a service networking platform that helps manage and secure communication between microservices in on-prem, hybrid, and multi-cloud architectures. It supports Kubernetes and provides a control plane for automating management of traffic between services through features like service discovery, DNS, load balancing, and routing. Datadog Network Performance Monitoring (NPM) offers visibility into network flow, connections, dependencies, and traffic volume, enabling users to quickly troubleshoot application errors and latency. NPM can be used to monitor traffic between microservices managed by Consul and ensure the health of Envoy proxies. It also brings together network, application, and infrastructure data for monitoring Consul in context.

This article explains how to use Datadog to enhance the monitoring and visualization of Consul metrics and logs. Datadog, with its over 650 integrations, offers deep visibility into Consul's service discovery and dynamic configuration features by allowing users to set up custom dashboards, analyze logs, and configure alerts for the cluster's availability and performance. The Datadog Agent, an open-source tool, collects metrics from Consul via two primary methods: direct metric transmission from Consul and querying Consul’s HTTP API for node and service status. By configuring Consul to send metrics to the Datadog Agent using DogStatsD, users can retain these metrics for longer periods and enable detailed analysis. The article highlights the steps for setting up Consul integration with Datadog, including enabling log collection and network monitoring, and emphasizes the benefits of using Datadog's Host Map, Network Performance Monitoring, and customizable dashboards for gaining insights into Consul's health and performance. Additionally, Datadog's alerting capabilities allow users to receive notifications for potential issues such as failing health checks and frequent leadership transitions, providing a comprehensive monitoring solution for Consul clusters.

Datadog's Network Performance Monitoring (NPM) feature provides visibility into the flow of traffic in your network, allowing you to quickly troubleshoot application errors and latency. It can help identify issues with Consul service mesh configuration, such as incorrect security settings or expired TLS certificates, and provide insights into the health of Envoy proxies. The feature also gives you deep context into your Consul cluster's performance side by side with monitoring data from applications and infrastructure throughout your stack, making it easy to see whether application errors or latency are tied to the health and performance of your Consul cluster. With Datadog NPM, you can monitor traffic between microservices managed by Consul, ensure the health of Envoy proxies, and correlate and contextualize your Consul monitoring data.

AWS re:Invent 2021 was a hybrid event that brought together cloud computing professionals to share insights and solutions. The focus this year included health and technology, diversity, equity, and inclusion, as well as security, accessibility, and refinement in cloud technologies. Datadog participated with three booths showcasing its latest innovations and machine learning capabilities. Other highlights from the event include updates on existing products and platforms, low-code and no-code solutions for empowering users less inclined to software engineering, and a strong emphasis on security across all presentations and demos.

AWS re:Invent 2021 was a conference that saw a focus on health and technology, diversity, equity, and inclusion, security, accessibility, and refinement in cloud technologies. The event featured talks from Datadog and partners, including Nathan Case, Kirk Kaiser, and Jeff Nickoloff, who discussed topics such as automating instrumentation, bringing intentional and empathetic observability to dashboards, and making machine learning accessible to everyone. The conference also showcased refinements and updates to existing products and platforms, with a focus on new features and updates that demonstrate the success of cloud technology over the last decade. Additionally, there was a significant product pivot towards creating low-code and no-code solutions to empower users who are less inclined to software engineering. New low-code solutions such as Amazon SageMaker Canvas and SageMaker Studio Lab were also announced, aiming to break down silos across the machine learning pipeline and make machine learning accessible to everyone. Security was a major theme, with many presentations on security-focused content, including Access Analyzer, which analyzes policies in AWS Organizations. Observability was also front and center, with products like Datadog paving the road to build observability into the very DNA of cloud services.

Dell EMC Isilon is a petabyte-scale network attached storage (NAS) system that allows users to archive unstructured data and operates in a cluster for high availability. The integration of Crest Data Systems' Dell EMC Isilon monitoring from the Datadog Marketplace enables tracking the health of all Isilon clusters, monitoring individual nodes, and seeing how Islon's performance affects applications. This integration also allows users to provide Isilon monitoring access without granting them access to the cluster.

Neha Shah and David M. Lentz from Dell EMC Isilon announce that their petabyte-scale network attached storage (NAS) system is now available in the Datadog Marketplace, allowing users to track the health of all Isilon clusters on five out-of-the-box dashboards revolving around cluster, quota, file system, protocol, and node information. This integration also enables users to provide Isilon monitoring access to users without granting them access to the cluster. The solution helps users detect and troubleshoot bottlenecks in their Isilon clusters by monitoring cluster performance and correlating application metrics with Isilon metrics. It provides a quick view of one important dimension of the cluster's health, such as node pool throughput and utilization, CPU usage, throughput, and connection rate. Users can also customize the dashboard to track related metrics from the rest of their infrastructure and other applications. Additionally, the integration allows users to start monitoring Isilon in the Datadog Marketplace with a 14-day free trial, including a 14-day free trial.

In this interview with Uroš Gruber, a maintainer of the FreeBSD port of the Datadog Agent and founder of Squarebox, he shares his journey into technology from the 1990s to the present day. He discusses the tech scene in Slovenia, his early experiences with computers like ZX Spectrum and Pentium, and how he got hooked on FreeBSD due to its stability and well-documented framework. Uroš also delves into the process of porting software to FreeBSD, specifically the challenges faced while porting the Datadog Agent, which involved understanding Golang and Python integration, handling permissions, and dealing with configuration files and default integrations. He expresses his desire for more contributions in developing features like support for FreeBSD jails.

Daniel Maher and Uroš Gruber discuss FreeBSD, a Unix-like operating system. Uroš, the maintainer of the Datadog Agent port for FreeBSD, shares his experience with porting software to the platform. He highlights the challenges he faced, including differences in file paths, variable replacement, and configuration files. Despite these difficulties, Uroš successfully completed the port and made several patches to improve the integration with FreeBSD. He also expresses interest in contributing to other projects, such as supporting FreeBSD jails. The conversation showcases Uroš's passion for technology and his willingness to learn and adapt to new challenges.

In a virtual discussion, Uroš Gruber, a Slovenian technologist and maintainer of the FreeBSD port of the Datadog Agent, shares insights into his career and the Slovenian tech scene. Gruber discusses his journey from early tech encounters with a ZX Spectrum to developing for FreeBSD, emphasizing his preference for FreeBSD's stability and documentation over Linux. He explains the complexities involved in porting the Datadog Agent to FreeBSD, which included dealing with Golang and Python integration and adapting software paths. Despite challenges, Gruber highlights the enjoyment he found in the process, the contributions of other developers, and the potential for future enhancements like FreeBSD jail support. The interview underscores the collaborative spirit in tech development and provides a glimpse into the evolving technological landscape in Slovenia.

The text discusses how implementing shift-left testing with Datadog Synthetic CI/CD Testing can help teams prevent faulty code deployments and improve end-user experience. It explains how to set up the new GitHub Action for running Synthetic tests within workflows, and how to monitor these tests using Datadog's CI Results Explorer and CI Visibility features. The integration of Datadog with GitHub Actions allows teams to quickly troubleshoot failed tests and gain insights into the health and performance of their pipelines.

Datadog has introduced a new GitHub Action that enables teams to implement shift-left testing throughout their CI/CD pipeline, allowing them to prevent faulty code deployments from degrading the end-user experience. This action integrates with Datadog Synthetic Monitoring, enabling quick troubleshooting of failed tests within workflows and providing visibility into the health and performance of pipelines. By using this new GitHub Action, teams can easily monitor their GitHub Actions workflows in Datadog, troubleshoot problematic builds and stages, identify flaky tests, and gain valuable insights into pipeline health, ultimately saving time and ensuring smoother builds. The action is now available in the GitHub Marketplace for easy installation, making it simple to add Synthetic tests to existing or new GitHub workflows.

Microsoft Azure SQL Database is a fully managed platform-as-a-service (PaaS) database offering that supports both relational and non-relational data models. It provides various purchase models and service tiers to accommodate different types of workloads, including single databases, elastic pools, serverless databases, and more. Azure SQL Database generates telemetry data such as metrics, resource logs, and audit logs to help monitor performance and costs. Key performance metrics include compute, storage, request, and connectivity metrics, which provide insights into resource utilization, availability, and database traffic. Monitoring these metrics can help detect potential issues and optimize the configuration of Azure SQL databases for better performance and cost efficiency.

Datadog offers a comprehensive solution for monitoring Azure SQL databases by seamlessly integrating with the Azure platform, allowing users to track performance metrics, analyze logs, and detect potential security threats. This integration enables the collection of telemetry data from various Azure resources, facilitating the visualization of key metrics and the creation of alerts for database performance issues, such as inefficient queries or long-running processes. By exporting diagnostic and audit logs to Datadog, users can gain insights into database activity and security risks, leveraging Datadog's built-in detection rules to identify unauthorized access or SQL injection attempts. The platform also provides customizable dashboards for tracking database performance in conjunction with other Azure services, helping users pinpoint the root causes of performance anomalies and ensuring that database instances continue supporting applications effectively.

In Part 2 of the series on monitoring Microsoft Azure SQL databases, we discussed how Azure Monitor provides visibility into the health and performance of your databases through its platform tools like alerts and Metrics Explorer. We also covered how to collect metrics and logs from database instances and monitor them using Azure's monitoring and reporting tools. Additionally, we explained how to enable diagnostics and auditing on databases in order to start collecting database metrics and logs. Finally, we looked at various Azure Monitor features for querying, analyzing, and alerting on key database metrics and logs.

Azure SQL Database is a fully managed platform-as-a-service (PaaS) database offering that runs on the latest version of the SQL Server database engine, enabling highly available and performant database instances without hardware upgrades or maintenance. It supports both relational and non-relational data models, allowing for consolidation and management of different data formats in a single database instance. Azure provides various purchase models and service tiers to support different workloads, including database transaction units (DTUs) and virtual cores (vCores), which dictate resource limits and costs. Monitoring Azure SQL databases helps manage costs and detect performance issues, with telemetry data including metrics on resource utilization, deadlocks, and audit logs. Key performance metrics include compute, storage, worker usage, and limits, as well as connectivity metrics such as active connections to a database. The platform provides features like threat detection alerts, auditing, and logging to protect databases from security threats and ensure compliance with regulatory requirements. By monitoring key metrics and logs, users can gain better visibility into their Azure SQL databases' health and performance, enabling data-driven decision-making to optimize costs and improve application reliability.

Azure Monitor provides visibility into the health and performance of Azure SQL Database instances, allowing users to collect metrics and logs from databases and monitor them with Azure's monitoring and reporting tools. The platform uses dedicated data stores to collect metrics and logs from Azure resources, retaining metrics for 93 days by default. To take advantage of all of Azure Monitor's features, database instances must be configured to write metrics and logs to a Log Analytics workspace. Users can enable diagnostics and auditing on databases to start collecting database metrics and logs, and set up auditing policies at either the server or database level. Once enabled, users can leverage all of Azure Monitor's monitoring and reporting capabilities to view and analyze database metrics and logs, including using the Azure SQL Analytics monitoring solution, creating custom alerts, and reviewing audit logs in Log Analytics.

Datadog, a company that values individual perspectives and contributions, has launched its new Datadog Spotlight Series to feature employees from teams around the globe. In this series, Geoffrey Kwan, a Solutions Engineer based in New York, shares his career journey at Datadog. Starting as a University Recruiter, Geoffrey transitioned into a Solutions Engineering role after learning about it from colleagues. As a Solutions Engineer, he helps customers navigate the product and solve monitoring challenges specific to their business. Geoffrey has grown and developed significantly in his career at Datadog, including becoming a Community Guild co-leader for the Asian and Pacific Islander Community. Datadog is currently hiring for various roles worldwide.

At Datadog, a company that values diversity and individual contributions, the team is growing rapidly and celebrating its success with a new Spotlight Series. The series features stories from employees around the globe, including Geoffrey Kwan, a Solutions Engineer based in New York City. Kwan shares his journey to Datadog, starting as a University Recruiter and transitioning into a Solutions Engineering role, where he helps customers navigate the platform and solves monitoring challenges specific to their business. As a Solutions Engineer, Kwan emphasizes the importance of continuous learning and growth alongside the product, and highlights the support he received from colleagues and leadership throughout his career development. He also shares his experience as a Community Guild co-leader at Datadog, where he advocates for diversity, equity, and inclusion in the workplace and provides resources to the Asian and Pacific Islander community. The company invites others to join its teams around the world by learning more about open roles on its Careers page.