LOG MANAGEMENT IN JUST 3 DAYS FOR A FLEMISH HOSPITAL GROUP

With Elastic Cloud, we set up a powerful cluster in just a few simple steps. The process starts by creating a cluster in Elastic Cloud, specifying desired configurations, the number of nodes, and available resources. Elastic Cloud takes care of the underlying infrastructure, allowing us to focus on the optimal use of Elasticsearch. The flexibility provided by Elastic Cloud is advantageous. We can easily scale up or down depending on our needs, whether adding extra nodes for more storage capacity or adjusting computing power for better performance. Elastic Cloud makes the process effortless and efficient.

We quickly integrated the Fleet Server, a crucial part of our log management process, into Elastic Cloud. With Fleet Server, we can manage Elastic Agents at scale, with an intuitive interface that simplifies deployment, updates, and monitoring.

The implementation of Fleet Server in Elastic Cloud has given us the flexibility to centrally manage agents, regardless of their physical location. With just a few simple commands, we ensured that Elastic Agents operate synchronously and effectively, resulting in streamlined and optimized log data collection.

This Elastic Stack utilizes a thoughtful combination of Fleet Managed Elastic Agents and Logstash, creating an integrated and scalable approach to managing and analyzing log data within the hospital group. In a short period of only 3 days, we introduced an Elastic Stack implementation that not only embraces efficiency and speed but also seamlessly aligns with the unique needs of a hospital environment

With Elastic Cloud Fleet, you don’t need to manually configure agents on each machine. Instead, Fleet provides a central interface for effortlessly deploying Elastic Agents across various machines, all with just a few simple commands.

The Fleet Server acts as the conductor, coordinating and managing Elastic Agents. It not only accelerates the deployment process but also makes it easy to apply configurations and monitor the status of agents. Fleet Server enables large-scale deployment, allowing agents to do their job without consuming valuable time and effort.

Elastic Agents act as digital watchdogs deployed to gather crucial information from different systems in the IT landscape of the Great Flemish Hospital Group.

The use of policies and Windows integration significantly reduces the need for extensive manual configurations. With this integration, default settings and configurations for Windows logs are automatically applied, allowing you to quickly and efficiently collect valuable log data without extensive manual adjustments. This streamlines the process and minimizes the time and effort required for manual configurations. Think of them as reliable monitors continuously monitoring logs and data for important insights. These agents are designed for efficiency, ensuring that all relevant information is securely captured.

Logstash acts as the digital transformer of raw, unstructured data into an understandable format. Imagine it receiving raw data from various network devices and organizing it into a unified language. This purifies and structures the raw data, ready for further analysis. A local Logstash installation also reduces the load on the Elastic Cloud system by refining raw data before it moves to the next phase

Elastic Agents, Fleet Server, and Logstash work together as a well-oiled mechanism. Elastic Agents collect valuable information, Fleet Server centrally coordinates and manages these agents, and Logstash transforms raw data from network devices into understandable information. Together, they form a powerful solution that enables the Great Flemish Hospital Group to deal with log data quickly, securely, and effectively.

To gain a comprehensive insight into the logs and performance data of Windows-based systems in this hospital group, we deployed Elastic Agents with Windows Integrations.

Deploying Elastic Agents with Windows Integrations was a crucial step in the log management process. With speed and efficiency, we implemented these agents to serve as valuable sources for capturing logs and performance data from Windows-based systems.

By deploying Elastic Agents with specific Windows Integrations, we ensured that no detail was lost. Whether it’s system logs, domain controller logs, or exchange server logs, these agents provide a holistic overview essential for thorough analysis and monitoring

In managing log data from various network devices, Logstash plays a central role as the digital transformer.

To effectively direct logs from diverse network devices to Logstash, we set up syslog forwarders.

The logs from various network devices are first sent to Logstash for parsing and transformations, converting raw, unstructured data into a suitable format for analysis before forwarding it to Elastic Cloud. Logstash serves as the central point for log processing. Centralized management of log transformations in Logstash also reduces the load on Elastic Cloud, making it more efficiently utilized.

Additionally, the use of local Logstash reduces resource costs by minimizing the load on the Elastic Cloud Deployment. This allows Elastic Cloud to focus on indexing and searches, resulting in a cost-effective implementation for log management.

The heart of our strategy was to create an integrated environment for managing and analyzing log data. This solution enabled us to manage, transform, and analyze logs within a short timeframe.

The integrated Fleet Server formed the pivot in our log management process. With this powerful tool, we could efficiently manage Elastic Agents at scale. The straightforward implementation, updates, and monitoring via Fleet Server resulted in rapid and large-scale deployment of Elastic Agents, translating into significant time savings and reduced efforts.

Logstash acted as our digital transformer, where raw, unstructured data was converted into a structured format ready for in-depth analysis. The use of Logstash not only contributed to optimizing log transformation but also provided cost-efficiency by refining raw data before sending it to Elastic Cloud.

By uniting these components, we created an integrated, scalable solution for managing, transforming, and analyzing log data within a timeframe of just 3 days.