Operations | Monitoring | ITSM | DevOps | Cloud

When a parser breaks at 10:06, the first question is who changed what at 10:05. Every Logit.io Logs stack keeps an audit log that answers it, with the user, the IP address and the action. This guide shows how to read and export it, how to pull it from the Dashboard API, and how to ship it with Filebeat into logstash-* so it sits on the same OpenSearch Dashboards timeline as your application logs. Contents.

You have a timeout in your application logs and a question in your AI assistant: what happened to this request? Hosted OpenSearch MCP works with any MCP-compatible AI client or model. Claude Desktop and Cursor are examples. This guide covers the Basic-auth prerequisite, client configuration and a first log search you can verify against records you already recognise. Contents.

Distributed tracing has become essential for understanding the performance and behavior of modern microservices architectures. As applications become more complex with multiple services communicating across different environments, traditional logging and metrics alone are insufficient for debugging performance issues and understanding request flows.

Server monitoring is essential for organisations relying on digital infrastructure. It tracks performance, availability, and health to ensure smooth operations. Without it, businesses face risks like downtime, poor performance, and security threats.

Audit logs, otherwise referred to as audit trails, are detailed records that document activities or a sequence of activities or events. Typically, they deal with the usage of systems, applications, and/or networks. They are crucial in ensuring security, compliance, and operational oversight and enable users to keep track of the history of all actions executed and who has done what and when.

As data continues to grow exponentially, businesses need powerful tools to search, analyze, and visualize their data efficiently. OpenSearch has emerged as a top choice for organizations seeking an open-source, scalable search and analytics engine. However, managing OpenSearch in-house can be complex, costly, and resource-intensive. That’s where hosted OpenSearch comes in.

Kafka is an open-source distributed streaming platform for high-throughput and fault-tolerant real-time data streaming in large-scale systems. It can integrate with a wide range of data sources and sinks, which include databases, message queues, big data processing frameworks like Apache Spark and Apache Flink, and many more.

Time series data is particularly prevalent, seen across numerous different industries and use cases. It offers significant value to various organizations, highlighting the importance of effectively monitoring and analyzing the data. By analyzing and monitoring time series data you can understand trends, patterns, and anomalies in sequential data collected at many points in time.

If you’re considering OpenSearch for your search and analytics infrastructure, the first question that likely comes to mind is: what will it cost? OpenSearch, the powerful, open-source search engine and analytics platform, provides a highly scalable solution for businesses. However, while the software itself is free to use, there are still costs associated with hosting, maintaining, and scaling OpenSearch clusters.

For organizations, it is crucial to swiftly respond to evolving market dynamics, shifting customer preferences, and emerging operational challenges. This responsiveness is made possible through the use of real-time data streaming technologies, which provide a dynamic and profound understanding of the environment. In this article, we will outline why real-time data streaming is beneficial before listing the leading real-time data streaming tools currently available.