The Azure Cloud platform processes an extensive variety of data including Eventhub Diagnostic Logs, Kubernetes Metrics, SQL Logs, Activity Logs, Container Activity Logs, and Azure Metrics. Depending on the requirements of your organization these logs offer various levels of importance and priority. But it’s more than likely that you will be monitoring a large variety of these logs.

RabbitMQ, Apache Kafka, and Redis are some of the most popular microservices message brokers on the market. However, while they’re all the same type of tool, they each offer different features that make them better adapted for specific use cases. To further understand this, in this article, we will outline the main similarities and differences between these tools and highlight which is the best tool for various use cases.

IIS (Internet Information Services) is a web server developed by Microsft, shipped as a part of the Windows Server services. It’s used to host and manage web applications and services. IIS is a particularly robust web server solution that is tightly integrated with the Windows operating system, making it a natural choice for organizations that rely on other Microsoft products.

Logging is an essential part of development. While working on React projects, logging provides a way to get feedback and information about what’s happening within the running code. However, once an app or website is deployed into production, the default console provides no way to continue benefiting from logs.

Understanding telemetry signals for better decision-making, improved performance, and enhanced customer experiences Telemetry signals have evolved significantly over the years — if you blinked, you could have missed it. In fact, much of the common wisdom about observability needs a refresh. If your observability solution doesn’t consider the current state of telemetry, you might need an upgrade.

The combination of AI and search enables new levels of enterprise intelligence, with technologies such as natural language processing (NLP), machine learning (ML)-based relevancy, vector/semantic search, and large language models (LLMs) helping organizations finally unlock the value of unanalyzed data. Search and knowledge discovery technology is required for organizations to uncover, analyze, and utilize key data.

Migrating your data infrastructure can sound daunting, especially when you’re dealing with complex systems like OpenSearch. But what if it could be easier—almost ridiculously easy? If you’re thinking, “Hey, wait a second—could this be as seamless as it sounds?” You’re in for a pleasant surprise. In this blog, we’re diving into how moving and Simplifying Your Data Node Migration with Graylog makes the process smooth, secure, and efficient.

BigQuery’s pipe syntax introduces an intuitive, top-down syntax for understanding data transformations, and is used in Cloud Logging Log Analytics.

Imagine waking up to a critical system failure that has brought your business operations to a standstill. Panic sets in as you scramble to understand what went wrong. You sift through various error messages, desperately trying to pinpoint the issue.