Operations | Monitoring | ITSM | DevOps | Cloud

With thousands of logs generated every minute from your infrastructure, applications, services, and devices, retaining all of this data for active search and analysis can be cost-prohibitive. Because log volumes continue to grow rapidly as operations scale, it’s common for organizations to implement log management strategies and limit the amount that they store in order to minimize costs.

Modern applications increasingly rely on specialized databases and platforms to power real-time analytics and support advanced AI/ML capabilities. These tools help teams accelerate development by consolidating workflows and processes, enabling faster and more efficient data operations. That’s why Datadog has launched three new data platform integrations with Supabase, DuckDB, and Milvus.

Google Cloud Run is a fully managed service that allows you to deploy, manage, and scale workloads on serverless containers. Because Cloud Run abstracts away infrastructure management and runs on complex, distributed backends, it can be difficult to troubleshoot. Datadog’s integrations with Google Cloud and Google Cloud Run address that challenge by collecting and visualizing key metrics and logs.

Today’s SRE and security operations center (SOC) teams often find themselves overwhelmed by the sheer volume and variety of logs generated by critical AWS services such as VPC Flow Logs, AWS WAF, and Amazon CloudFront. While these logs can be valuable for detecting and investigating security threats, as well as troubleshooting issues in your environment, managing them at scale can be challenging and costly.

Datadog Database Monitoring (DBM) Recommendations help you proactively optimize performance throughout your database fleet. DBM draws on a wide range of data sources in order to detect and provide actionable guidance on issues such as blocking queries, low disk space, and missing indexes. In this post, we’ll show you how DBM formulates targeted indexing recommendations to help you optimize database performance.

Most modern web applications are frequently updated to implement new features, execute marketing campaigns, or enhance their UX with new libraries or APIs. While this helps you better engage your users, constant UI updates make designing flexible, long-lasting tests challenging.

As software ecosystems grow more complex and fragmented, organizations are finding it harder to manage the thousands of interdependencies that make up their environments. For starters, engineers are collectively struggling to uphold security and reliability standards throughout their organizations because they lack a shared view of these complex software landscapes.

Providing rich context for monitor alerts is an essential part of any robust, scalable monitoring strategy. Alerts that send teams scrambling for basic background information prolong troubleshooting, hindering effective incident response and heightening the potential for service disruption. Given the increasing complexity of modern, distributed applications, however, breaking down knowledge silos in order to ensure consistent access to critical context for alerts can be a challenge.

The rapidly growing interest in AI has raised a corresponding demand for specialized cloud compute that is built to run training and inference workloads in a cost-efficient and performant manner. Google Cloud Tensor Processing Units (TPUs) have become a popular accelerated compute solution for AI/ML workloads.

Data build tool (dbt) is an open source service that cleans, aggregates, and models raw data into organized, analytics-ready formats within a data warehouse. dbt Cloud, a fully managed platform by dbt Labs, extends dbt’s capabilities with advanced features such as scheduling, testing, and monitoring, accessible directly from your browser.