February 2018 Summaries

The AWS Serverless Application Repository is a new service that enables companies, individuals, and partners to publish their serverless applications in a central repository for public use. Datadog has launched two serverless applications on this platform - an RDS enhanced integration and a VPC Flow Log integration. These applications make it easier to capture important metrics and tags from AWS in Datadog. The RDS enhanced integration allows users to monitor high-resolution OS-level metrics from RDS instances running MySQL, Aurora, PostgreSQL, and MariaDB. The VPC Flow Logs integration captures information about the IP traffic flowing to and from a virtual private cloud's network interfaces. Both applications are deployed via Lambda functions in the AWS Serverless Application Repository.

The AWS Serverless Application Repository is a new service that allows developers to publish and deploy serverless applications on AWS, using the Serverless Application Model (SAM) template. Datadog has included two serverless applications in its launch: an RDS enhanced integration and a VPC Flow Log integration, making it easier for customers to monitor high-resolution OS-level metrics from RDS instances and capture IP traffic flowing to and from their virtual private cloud's network interfaces. These integrations provide valuable insights into AWS environment performance and security, allowing developers to quickly deploy serverless applications on AWS without manual setup or configuration steps. The integrations can be found in the Serverless Application Repository, and Datadog offers a 14-day free trial for new users to try out its serverless applications.

The first-ever Dash conference is being held on July 11 and 12 in New York City, bringing together over 1,000 engineers to discuss building and scaling next-generation applications, infrastructure, and technical teams. The two-day event features a mix of keynotes, breakout sessions, hands-on labs, and trainings, with tracks focused on performance, scalability, and team growth. Notable speakers from companies like Google, Shopify, and Airbnb will share their expertise on developing scalable, resilient, high-performance applications and teams, including lessons learned from shifting to microservices-based architectures and optimizing service level indicators. Registration is now open, offering early bird pricing and discounted hotel rooms near the venue.

AWS Health is a service that provides continuous visibility into the status of an entire AWS environment, delivering near-real-time alerts in response to changes in the health of AWS resources. Datadog's new integration helps monitor the health of an AWS environment by automatically creating rich, contextual events from the AWS Health API. This integration allows users to easily correlate AWS Health status events with metrics and events from other infrastructure technologies. By capturing metadata about affected entities and setting up event alerts, users can quickly respond to service issues and avoid unwanted downtime. However, this integration is only available to AWS Support customers who have a Business or Enterprise support plan.

The AWS Health service provides continuous visibility into the status of an organization's entire AWS environment, delivering near-real-time alerts in response to changes in resource health. Datadog's new integration with AWS Health allows users to monitor their AWS environment by automatically creating rich, contextual events from the AWS Health API, correlating these events with metrics and events from other infrastructure technologies in one place. The integration captures metadata about affected entities, including AWS account numbers, entity ARNs, and status codes, to create context-rich events in Datadog. This information enables users to quickly troubleshoot issues affecting specific resources throughout their infrastructure and applications. The integration also allows for event alerts based on string matching, tags, and more, enabling near-real-time notifications when AWS is experiencing a problem, which can be used to implement failover solutions and avoid downtime.

Amazon's Elastic Load Balancing (ELB) is an integral part of the AWS platform that distributes incoming traffic across EC2 instances. ELBs reduce maximum load on individual hosts, increase fault tolerance, and provide a single client-side connection point. There are three types of ELBs: Classic ELBs, Application Load Balancers (ALBs), and Network Load Balancers (NLBs). AWS NLBs differ from other ELBs in that they route incoming client requests at the TCP connection level using connection header details to determine which target to connect the client to. Monitoring NLBs is crucial for maintaining application functionality, as any issues may result in loss of application functionality. Datadog's AWS integration allows users to collect and analyze NLB metrics alongside other integrations, including Classic ELBs, ALBs, and the rest of the AWS platform. Key AWS NLB metrics include network traffic, reset packets, and host health. Monitoring these metrics can help identify potential issues in your application more efficiently.

Amazon's Elastic Load Balancing (ELB) allows for the distribution of incoming traffic requests across EC2 instances, reducing maximum load on individual hosts and increasing fault tolerance. The three types of ELBs offered by Amazon are Classic ELBs, Application Load Balancers (ALBs), and Network Load Balancers (NLBs). NLBs route incoming client requests at the TCP connection level, using connection header details to determine which target to connect the client to. Datadog's AWS integration now allows users to collect and analyze NLB metrics alongside more than 850 other integrations, including Classic ELBs, ALBs, and the rest of the AWS platform. The integration comes with a customizable dashboard that enables users to start monitoring their NLB metrics right away, along with relevant tags provided by Amazon CloudWatch. Datadog's NLB integration allows users to monitor network traffic, reset packets, and host health, providing insights into key network traffic metrics to help troubleshoot possible issues in applications more efficiently. The integration also enables users to compare and correlate network traffic with metrics across other components of their infrastructure to determine which resources need to be scaled accordingly.

Datadog has expanded its Application Performance Monitoring (APM) and distributed tracing support to include Java in addition to Ruby, Python, and Go applications. The new features allow for end-to-end monitoring of Java applications, including automatic tracing for web servers like Tomcat and Jetty, frameworks such as Spring Boot and Dropwizard, and data stores like MongoDB and Cassandra. Datadog APM also provides detailed performance overviews, automated alerts, and the ability to create custom dashboards that combine metrics from every layer of the stack. The integration with Java is easy to set up and includes built-in support for OpenTracing, a vendor-neutral standard for distributed tracing.

Datadog has announced the addition of support for Java to its application performance monitoring (APM) platform, which now includes out-of-the-box support for automatically tracing requests to web servers and frameworks like Spring Boot. With this update, users can gain end-to-end visibility into their Java applications, including detailed performance overviews, automated alerts, and ability to unify views across the entire stack. The APM platform also enables users to investigate and debug errors with additional context, and includes easy setup options such as deploying in Docker or installing the Datadog Agent and Java client on application servers.