In the context of application performance monitoring (APM) and observability, traces and spans are fundamental concepts that help users to track and understand the flow of requests and operations within a system. They are essential in assisting users to identify bottlenecks, troubleshoot issues, and optimize application performance.

MLOps stands for Machine Learning Operations. MLOps refers to the set of practices and tools that facilitate the end-to-end lifecycle management of machine learning models, from development and training to deployment and monitoring. The primary objective of MLOps tools is to address the unique challenges associated with deploying and managing machine learning models in real-world scenarios.

In this guide, we will compare two of the leading data visualization tools based upon open-source software that are available for use for metrics, traces and log analysis. To allow new users to know exactly which solution may be best suited to their needs, we wanted to explore in more depth a comparison between OpenSearch Dashboards and Kibana across various aspects in our latest guide covering the differences between leading open-source software.

Container monitoring refers to the process of monitoring and managing containers deployed within a containerization platform, such as Docker or Kubernetes. As containerization has become increasingly popular in software development and deployment, monitoring and managing containerized environments has become increasingly important.

Cloud monitoring tools are utilized to gather an extensive range of metrics and logs from cloud resources and services. Some commonly monitored metrics include CPU utilization, memory usage, network traffic, disk I/O, latency, and response time. By monitoring these metrics, among others, it becomes possible to gain insights into resource utilization, identify performance bottlenecks, and ensure that the infrastructure operates according to expectations.

Generally, data monitoring can be referred to as a continuous process of observing and tracking data in order to ensure its integrity, quality, and conformance with specific standards or requirements. Data monitoring often involves systematic data collection, analysis, and reporting to identify patterns, trends, anomalies, and potential issues.

There are many advantages to using dashboards that are powered by open-source technology that make them a compelling choice for many organizations. Below we will discuss some of the major benefits of using dashboards that are built with the help of open-source technology, along with examples of some of the leading use cases for which open-source technology has been utilized.

InfluxDB is a powerful tool for managing time-series data. It is widely used in industries such as IoT, finance, healthcare, and more. Using InfluxDB, you can query and store large amounts of data in real-time, making it easier to identify patterns, trends, and anomalies. InfluxDB dashboards provide a comprehensive overview of your system performance, metrics, and KPIs in real-time. You can customize these dashboards to meet your specific requirements.

Logit.io is pleased to introduce the latest version of OpenSearch onto the platform, with an OpenTelemetry-compliant data schema that unlocks a host of future analytics and observability capabilities. Also included in this release are improvements in threat detection for security analytics workloads, visualization tools, and machine learning (ML) models.