Operations | Monitoring | ITSM | DevOps | Cloud

Microsoft Fabric is Microsoft’s new platform for all things data analytics—integrating key Azure data analysis products like Azure Data Factory, Azure Synapse, and Power BI into a unified platform. Fabric is intended to provide a one-stop shop where users with various levels of expertise across an organization can perform data analysis and collect insights.

Anthropic is an AI research and development company focused on building reliable and safe artificial intelligence systems. Their flagship product is Claude, an advanced language model and conversational AI assistant known for its strong capabilities in natural language processing, reasoning, and task completion. Anthropic places a particular emphasis on AI safety and ethics, and its models and APIs are used by organizations across various industries to build powerful, safe, and performant AI applications.

ServiceNow is a popular IT service management platform that helps organizations track and manage enterprise-level IT processes, such as on-prem infrastructure management, customer support, and incident response. By using ServiceNow’s configuration management database (CMDB), organizations can easily centralize and manage information about all the IT objects they own in order to track and maintain them more efficiently.

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.

Amazon MemoryDB for Redis is a highly durable in-memory database service that uses cross-availability-zone data storage and fast failover, providing microsecond read times and single-digit-millisecond write times. Datadog’s integration for MemoryDB uses a range of metrics to provide important visibility into MemoryDB performance.

Understanding costs is an essential part of service ownership. But in cloud-based applications, the cost of any given service often comes down to a wide range of dynamic factors. Individual services can incur fees from numerous dependencies, from data stores to observability solutions, and keeping track of these expenses can mean reckoning with the intricacies of many different billing models.

Today’s distributed IT infrastructure consists of many services, systems, and applications, each generating logs in different formats. These logs contain layers of important information used for data analytics, security monitoring, and application debugging. However, extracting valuable insights from raw logs is complex, requiring teams to first transform the logs into a well-known format for easier search and analysis.

A few months ago, we implemented support for exception profiling in PHP. One of the key justifications for building this functionality into Continuous Profiler was to show the hidden costs of exceptions in PHP, especially when they are used for flow control in hot code paths. Once this feature was built, we naturally wanted to know if it surfaced these kinds of flow control problems in customer production systems.

The proliferation of managed LLM services like OpenAI, Amazon Bedrock, and Anthropic have introduced a wealth of possibilities for generative AI applications. Application engineers are increasingly creating chain-based architectures and using prompt engineering techniques to build LLM applications for their specific use cases.

Widespread adoption of containerized infrastructure has been closely followed by an explosion of container-native tools for each layer of the stack, including new solutions for managing CI/CD pipelines in container-based environments, such as the Argo suite, FluxCD, and Tekton. This is because these lightweight solutions make it easier to automate builds, testing, deployments, and more on Kubernetes, as well as other platforms that manage containerized workloads and services.