Operations | Monitoring | ITSM | DevOps | Cloud

Prometheus is an open-source monitoring solution, it offers efficient, scalable, and flexible monitoring practices and has emerged as a trusted tool for organizations seeking insights into their systems. It’s written in Go, gathers metrics data, and stores it in a time series database. Also, Prometheus employs a robust query language, PromQL, to manipulate and analyze collected time series data, offering versatile monitoring capabilities for various systems and services.

AWS logs are fundamental for organizations to conduct performance analysis, troubleshooting, security monitoring, and adhere to compliance requirements. But if you’re using more than one AWS service you can quickly realize that your logs are expanding out of control across decentralized locations. Therefore it’s crucial that you can process and analyze all your AWS logs within a single centralized repository.

Guaranteeing that your systems, applications, networks, and servers are operating optimally and delivering the expected levels of performance and user experience is a crucial practice for all organizations. This process is called performance monitoring.

Database monitoring is crucial for numerous reasons, an example being that monitoring database performance metrics such as query execution times, throughput, and resource utilization helps highlight performance bottlenecks. By conducting this, administrators can enhance database configurations, queries, and indexing by examining these metrics to optimize overall performance.

Log Analysis has been a beneficial practice for organizations for numerous years, and over these years it has continuously evolved. This has been in part driven by the increasing volume of logs that companies are required to monitor. Now, log analysis is shifting again, incorporating machine learning (ML) and artificial intelligence (AI) to assist data analysts in identifying system log patterns and anomalies.

Java is a flexible and commonly used programming language known for its platform independence, object-oriented design, and robustness. It was originally developed by Sun Microsystems (now owned by Oracle Corporation) in the mid-1990s and soon gained popularity due to its "Write Once, Run Anywhere" (WORA) principle, allowing developers to write code that can operate on any device or platform with a Java Virtual Machine (JVM).

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.

OpenSearch allows you to store a sizeable amount of data, commonly logs, metrics, and documents. You access useful data within OpenSearch by querying to get specific information, deep analysis, and insights for decision-making. With OpenSearch, you can perform complex searches by using natural language, Boolean operators, and filters to pinpoint relevant information efficiently.

Docker is an open-source container service provider, designed to help developers build, run, and share container applications. Users building and running these container applications need to conduct effective debugging and monitoring practices and for this, they have turned to Docker logging. To understand the importance of this, the latest edition of our how-to guide series surrounds Docker.