AWS Lambda supports nearly any programming language by enabling developers to run serverless functions with either supported or custom runtimes. Once a runtime is deprecated, however, AWS will set dates for when you can no longer create or update functions using that runtime. You will then need to decide what course of action to take to ensure your Lambda functions continue running as expected.

As your IT environment scales, a proactive approach to monitoring becomes increasingly critical. If your infrastructure environment contains multiple service dependencies, disparate systems, or a busy CI/CD application delivery pipeline, overlooked anomalies can result in a domino effect that leads to unplanned downtime and an adverse impact on users.

Windows Blue Screen errors—also known as bug checks, STOP codes, kernel errors, or the Blue Screen of Death (BSOD)—are triggered when the operating system detects a critical issue that compromises system stability. To prevent further damage or data corruption, the OS determines that the safest course of action is to shut down immediately. The system then restarts and displays the well-known BSOD.

Web applications emit a wealth of metadata and user interaction information that’s critical to understanding user behavior. However, parsing this data to find what is most relevant to your product analytics project can be challenging—what one product analyst might find useful, another might consider unnecessary noise.

Custom resources are critical components in Kubernetes production environments. They enable users to tailor Kubernetes resources to their specific applications or infrastructure needs, automate processes through operators, simplify the management of complex applications, and integrate with non-native applications such as Kafka and Elasticsearch.

While overprovisioning Kubernetes workloads can provide stability during the launch of new products, it’s often only sustainable because large companies have substantial budgets and favorable deals with cloud providers. As highlighted in Datadog’s State of Cloud Costs report, cloud spending continues to grow, but a significant portion of that cost is often due to inefficiencies like overprovisioning.

Kubernetes offers the ability to scale infrastructure to accommodate fluctuating demand, enabling organizations to maintain availability and high performance during surges in traffic and reduce costs during lulls. But scaling comes with tradeoffs and must be done carefully to ensure teams are not over-provisioning their workloads or clusters. For example, organizations often struggle with overprovisioning in Kubernetes and wind up paying for resources that go unused.

As organizations increasingly turn to Kubernetes to support their cloud-native applications, it has become critical for teams to analyze and respond to the dense telemetry data related to this orchestration layer.

Azure AI Search is Microsoft Azure’s managed search service. In addition to tackling traditional search use cases, Azure AI Search also includes AI-powered features to make it a fully capable document catalog, search engine, and vector database. AI Search is highly interoperable—it can use models created in Azure OpenAI Service, Azure AI Studio, or Azure ML.

LLMs like GPT-4, Claude, and Llama are behind popular tools like intelligent assistants, customer service chatbots, natural language query interfaces, and many more. These solutions are incredibly useful, but they are often constrained by the information they were trained on. This often means that LLM applications are limited to providing generic responses that lack proprietary or context-specific knowledge, reducing their usefulness in specialized settings.