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.

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.

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.

When monitoring your logs metrics and traces it’s crucial that you can detect issues early to ensure the uptime of applications, alleviate bottlenecks, and enhance the performance of your systems. If you’re an experienced developer or IT professional this is a straightforward task when you’re viewing the data in front of you. However, when you aren’t viewing your data, it's just as important to guarantee that your systems are functioning optimally. This is achieved through alerts.

Log forwarding can be seen as the first step towards centralized log management. With centralized log management, your organization can gain from enhanced visibility, monitoring, and analysis capabilities, making it a coveted practice for numerous organizations. Log forwarding is crucial for maintaining robust IT security and operational efficiency, allowing organizations to manage and analyze logs from multiple systems in a centralized, scalable manner.

PostgreSQL PostgreSQL is a powerful open-source relational database management system (RDBMS) and is one of the most popular relational databases with over 1.5 billion users. It’s renowned for its reliability, robustness, and comprehensive capabilities. It is capable of managing a broad variety of workloads, from small single-machine applications to large-scale enterprise databases.