June 2021 Summaries

Shift-left testing is an approach that moves the testing phase earlier in the development life cycle, enabling agile teams to identify and resolve bugs before they become costly and time-consuming. This method has several advantages, such as automating continuous integration and delivery (CI/CD) workflows, encouraging collaboration between developers and testers, and improving application quality for end users. However, adopting shift-left testing requires substantial changes to existing processes, which can be challenging. To address these challenges, best practices include enabling developers to test their code, optimizing test infrastructure, creating builds that fail fast, and monitoring the performance of CI/CD pipelines. By implementing these practices, teams can improve development efficiency and reduce time to market for new features.

Datadog has introduced new muting features for monitors, allowing teams to filter out expected events during scheduled maintenance, testing, auto scaling events, and instance reboots. Users can now mute specific scopes of a monitor on an ad hoc basis using tags, schedule global mute settings with monitor downtiming, and benefit from automatic monitor downtiming for expected instance shutdowns in certain situations. These features help teams quickly pinpoint critical issues in their infrastructure by eliminating unnecessary alerting during planned events.

Shift-left testing is an approach to software development that involves moving the testing phase earlier in the development life cycle, allowing teams to identify and resolve bugs more quickly. This approach has several advantages, including identifying and resolving more bugs before they reach production, automating more of existing continuous integration and delivery workflows, and sharing ownership of testing responsibilities among team members. However, it requires substantial changes to existing processes and can be difficult to implement without adding overhead. To overcome these challenges, teams can adopt best practices such as enabling developers to test their code, optimizing test environments for efficient pipelines, designing pipelines to fail fast and provide actionable feedback, and monitoring pipeline performance. Datadog supports shift-left testing with features that enable running tests directly from CI/CD pipelines, monitoring CI pipelines and tests, and creating good feedback channels across teams. By adopting these best practices and leveraging tools like Datadog, teams can improve the quality of their applications and release new features to customers faster.

AWS CloudFormation is a service that enables infrastructure as code, allowing users to create templates for provisioning and managing resources such as EC2 instances, load balancers, and security groups. Datadog has collaborated with AWS to create additional resources available on the CloudFormation registry for use in templates, making it easier to manage Datadog components as code and gain real-time visibility into CloudFormation applications. Users can now add Datadog resources directly to their templates via the CloudFormation registry's public extensions, enabling automatic updates as new versions become available. These resources allow users to automate tasks such as enabling Datadog's AWS integration, creating, updating, and deleting monitors for services, scheduling downtime for monitors, managing users for a Datadog account, and creating and managing dashboards.

Datadog Cloud SIEM enables users to easily triage and alert on threats as they occur. By using webhooks integration, users can automate responses to common threats detected across their environments. Webhooks are HTTP callbacks that send messages to applications whenever a certain condition is met, allowing for the creation of simple automated remediation workflows that neutralize threats in real-time. Datadog's webhook integration makes it easy to set up webhook messages that deliver payloads to designated services when a Detection Rule is broken. Examples of using Datadog with webhooks to automate security responses include deleting misconfigured security groups, banning suspicious IP addresses, and auto-remediating application abuse and fraud.

Serverless applications streamline development by allowing developers to focus on writing and deploying code rather than managing infrastructure. To help monitor the performance of serverless applications, Datadog has released distributed tracing for AWS Lambda functions written in Python, Node.js, Go, and Java. This enables users to trace requests across their entire environment, including containers, hosts, and functions, providing insights into the health and performance of their serverless resources alongside the rest of their infrastructure. With support for Go and Java Lambda functions, Datadog APM helps developers gain real-time visibility into the performance of their serverless applications.

Datadog Cloud SIEM enables the automation of security responses to common threats by integrating with other services through webhooks, which act as script-based connectors that link Datadog to various tools. By setting up webhook messages that deliver payload information to downstream services, users can create simple, automated remediation workflows that neutralize threats in real-time. This integration allows for the deletion of misconfigured security groups, banning of suspicious IP addresses, and auto-remediation of application abuse and fraud. Datadog's webhook integration makes it easy to set up automation workflows that shave valuable time off threat-response workloads.

Datadog has released distributed tracing for AWS Lambda functions written in Python and Node.js, and now also supports Go and Java. This feature enables real-time visibility across serverless applications, allowing developers to monitor performance and identify code-level issues such as poorly performing goroutines or syntax problems in Java functions. The tracer automatically captures requests across all components of the environment, providing an end-to-end view of application performance. It can detect cold starts automatically and tag slow request traces with a `cold_start` attribute, making it easier to take mitigating actions. With support for Go and Java Lambda functions, Datadog APM helps developers gain real-time visibility into the performance of their serverless applications without requiring any code changes or configuration.

The Datadog mobile app introduces Mobile Incident Management, allowing users to declare incidents and notify responders directly from their mobile devices. This feature enables on-call professionals to manage issues without needing access to a laptop, improving productivity and response times. With the ability to gather a response team, view incident details, and collaborate with other platforms like Slack, Zoom, or Microsoft Teams, users can effectively address incidents while maintaining communication throughout the process. The app also provides an overview of ongoing incidents, sorted by severity and urgency, allowing professionals to focus on critical issues.

Databricks is an orchestration platform for Apache Spark that enables users to manage clusters and deploy Spark applications for efficient data storage and processing. By hosting it on cloud platforms like AWS, Azure, or Google Cloud Platform, one can easily provision Spark clusters to handle heavy workloads. Datadog's Databricks integration unifies infrastructure metrics, logs, and Spark performance metrics, providing real-time visibility into the health of nodes and jobs. This helps identify potential issues such as memory allocation and data partitioning inefficiencies. Deploying Datadog to Databricks clusters allows for monitoring job failures and making informed decisions for optimization. Monitoring infrastructure resource metrics from Databricks clusters, visualizing Spark job and stage metrics, and using logs to debug errors are crucial aspects of ensuring efficient performance and troubleshooting issues in Databricks.

The Datadog mobile app enables users to check alerts and dashboards from anywhere, allowing them to respond to issues more productively while on-the-go. Users can declare incidents and notify responders directly from their mobile device, providing a more efficient incident management experience. The app also allows users to quickly gather a response team, access incident details and monitors without needing a laptop, and get up-to-date information on active incidents. With Mobile Incident Management, users can be even more responsive and proactive in managing issues, regardless of where they are or what time it is.

Databricks is an orchestration platform for Apache Spark that enables users to manage clusters and deploy Spark applications for highly performant data storage and processing. Datadog's Databricks integration unifies infrastructure metrics, logs, and Spark performance metrics, providing real-time visibility into the health of nodes and performance of jobs. By deploying Datadog to Databricks clusters, users can monitor job failures, optimize cluster configuration, and troubleshoot problems using detailed system metrics, logs, and Spark metrics. The integration allows for a complete view of Databricks clusters, including resource usage across specific clusters, and enables fine-tuning of Spark jobs for peak performance. Additionally, Datadog's integration provides visibility into shuffle operations, node size, and proper partitioning to minimize the impact on job runs. Users can also use logs to debug errors and correlate node exceptions with performance metrics. The integration is designed to provide real-time visibility into Databricks clusters, ensuring they are available, appropriately provisioned, and able to execute jobs efficiently.

Datadog has introduced an integration that enhances visibility into Salesforce environments by collecting event logs through the Event Log File (ELF) API and real-time event monitoring, supporting over 50 event types via ELF and 14 via real-time streaming. This integration allows users to analyze logs for insights into security and operational performance, with Datadog automatically parsing key information for search, filter, and analysis. Users can specify relevant events to avoid extraneous data and use an out-of-the-box dashboard for visualization, which can be customized. The system enables monitoring of user activities, platform access, and security threats, providing alerts for suspicious behaviors such as anomalous user activity, API usage, and potential security threats like session hijacking. By aggregating logs and correlating them with data from over 850 other technologies, users can gain comprehensive insights into their Salesforce environment to ensure seamless operations and data security.

Datadog Cloud Network Monitoring (CNM) provides comprehensive visibility into cloud environments, helping users diagnose network issues, optimize traffic costs, and monitor the performance of cloud service dependencies. Migrating infrastructure to the cloud introduces challenges due to the ephemeral nature of cloud environments and shifting IP addresses, which can obscure visibility into both infrastructure and application levels. CNM addresses these challenges by enabling users to track network communication across cloud services, detect and diagnose network outages, and visualize live network traffic. It integrates with telemetry data, including distributed traces and logs, to help identify problems at both the network and service levels. Additionally, CNM aids in managing cloud traffic costs by visualizing traffic flows between availability zones and regions, allowing users to spot inefficiencies and make informed decisions regarding resource allocation. By providing a single interface for monitoring cloud infrastructure, CNM facilitates the identification of issues related to cloud provider performance and helps ensure efficient and compliant network operations.

AWS Service Quotas helps manage limits on resources or API operations for around 200 AWS services. Until recently, only a subset of these were available to monitor via CloudWatch metrics. However, AWS has announced support for control plane API usage metrics across AWS services, which are now automatically available in Datadog through its AWS integration. This allows users to track service quota utilization and take action before issues arise. Users can also clone and customize their AWS dashboards to monitor key control plane API usage metrics alongside performance metrics from their AWS services. By using existing Datadog metrics, users can automatically start tracking significant changes in control plane API calls in their AWS account and set up alerts for rate limiting or abnormal trends in API usage.

AWS Service Quotas helps manage limits on AWS resources and API operations. Recently, AWS announced CloudWatch support for control plane API usage metrics across services, making it easier to track service quota utilization and take action before issues arise. Datadog's existing `aws.usage.call_count.sum` metric can be filtered by service or resource to monitor specific API usage. This allows users to set up alerts for approaching rate limits and detect abnormal trends in API usage. With the integration of AWS with Datadog, users can start monitoring AWS control plane API usage metrics in their dashboards and alerts, enabling proactive management of operational disruptions.

Datadog's funnel analysis feature allows businesses to monitor user behavior and frontend performance by tracking how users navigate through their application's key workflows. By visualizing conversion rates across user journeys, companies can identify sources of friction in their app's user flows and determine where optimization is needed. Funnel analysis also integrates with Datadog RUM and Session Replay to provide a deeper understanding of user behavior within specific workflows, helping businesses investigate the root causes of user friction and improve overall application performance.

BigPanda, a powerful Event Correlation and Automation platform, has been made available through the Datadog Marketplace. This integration allows users to automatically correlate and analyze Datadog alerts, enriching them with contextual information for efficient incident management. By visualizing root causes in Datadog alongside monitoring data from across the stack, teams can quickly identify and resolve issues, preventing costly outages. The BigPanda offering is now available to all customers through the Datadog Marketplace.

BigPanda, an Event Correlation and Automation platform powered by AIOps, helps organizations detect and resolve incidents in their complex IT environments by unifying and correlating data from monitoring, change, and topology tools. This enables teams to quickly pinpoint the root cause of issues and prevent costly outages. The platform also automates the sharing of real-time incident details to analytic, ticketing, notification, and chat tools. BigPanda is now available through the Datadog Marketplace, allowing users to automatically correlate and analyze Datadog alerts and visualize incidents in Datadog with context-rich events. The integration streamlines incident management workflows by automating key administrative tasks during triage, creating tickets, notifying teams, and setting up war rooms. By promoting branded monitoring tools in the Datadog Marketplace, BigPanda benefits from membership in the Datadog Partner Network.