Operations | Monitoring | ITSM | DevOps | Cloud

Infrastructure changes often trigger incidents, but troubleshooting these incidents is challenging when responders have to navigate through multiple tools to correlate telemetry with configuration changes. This lack of unified observability leads to longer mean time to resolution (MTTR), greater operational stress, and ultimately, negative business outcomes.

Infrastructure changes often trigger incidents, but troubleshooting these incidents is challenging when responders have to navigate through multiple tools to correlate telemetry with configuration changes. This lack of unified observability leads to longer mean time to resolution (MTTR), greater operational stress, and ultimately, negative business outcomes.

When you face issues like reduced throughput or latency spikes in your production applications, determining the cause isn’t always straightforward. These kinds of performance problems might not arise for simple reasons such as under-provisioned resources; often, the root of the problem lies deep within an application’s runtime execution.

Data is central to any business: it powers mission-critical applications, informs business decisions, and supports the growing adoption of AI/ML models. As a result, data volumes are only increasing, and teams rely on engines like Apache Spark and managed platforms like Databricks or Amazon EMR to process this data at scale.

Datadog Actions help you respond to alerts and manage your infrastructure directly from within Datadog. This can be done by creating workflows that automate end-to-end processes or by using App Builder to build resource management tools and self-serve developer platforms. With more than 550 available actions, Datadog Actions offers capabilities such as creating Jira tickets, resizing autoscaling groups, and triggering GitHub pipelines.

When you’re responding to an issue with your application in the heat of on-call, you need reliable, well-maintained tooling that’s painless to use. Otherwise, the time you’ll spend combing through monitoring data for context, connecting to hosts and other infrastructure resources, and pivoting between consoles for various managed services can add up quickly and slow your response.

When troubleshooting an issue or remediating an outage, engineers need tools that are accessible and easy to use, closely integrated with their services, and tailored to their teams’ specific requirements.

The use of code profiling to troubleshoot application performance can appear daunting to the uninitiated, and many software engineers even assume that this domain is reserved for niche specialists. But here at Datadog, one of the key goals for our Continuous Profiler product has been to take this seemingly intimidating practice of code profiling and make it more accessible to engineers at all levels.

Organizations face significant challenges in increasing the efficiency of their growing cloud spending, even as the flexibility and variety of available cloud services offer many opportunities for optimization. Cloud environments are complex and dynamic due to the breadth of services and the drive to adopt new technologies, such as Arm-based processors and GPUs that enable AI capabilities.

AWS Batch on Fargate is an AWS offering that combines the benefits of AWS Fargate—a serverless compute engine for deploying and managing containers—with AWS Batch, a fully managed service for running batch workloads. Leveraging a pay-per-use pricing model and automatic scaling, AWS Batch on Fargate provides you with a cost-effective and scalable solution for running batch computing workloads without needing to worry about managing any underlying infrastructure.