Syslog is a widely used standard protocol for logging system events, errors, and other messages in computer systems. Syslog data can be incredibly valuable for detecting security breaches, identifying system errors, and tracking down performance issues. However, working with syslog data can be challenging, especially when dealing with large amounts of data. In this blog article, we'll explore the two main ways to work with syslog data: parsing the data and working with the raw data.
Parsing the syslog data
One way to work with syslog data is to parse it and create dashboards with real-time analytics. Parsing the data involves breaking it down into its constituent parts, such as the timestamp, source IP address, message type, and severity level. This parsed data can then be stored in a database, and visualized in dashboards using tools like Viewtinet.
The benefits of parsing syslog data include:
Real-time analytics: Dashboards can be created that provide real-time insights into system performance, security breaches, and other critical events.
Better searchability: Parsed data can be easily searched and filtered, making it easier to identify specific events or patterns in the data.
Reduced storage costs: Parsing data can often result in a smaller data footprint, which can help reduce storage costs.
Working with syslog raw data
Another way to work with syslog data is to work with the raw data. In this case, the analytics tool needs to understand the different available values per log. Raw data is the unprocessed data that comes directly from the syslog source, without any parsing or manipulation.
The benefits of working with raw syslog data include:
More flexibility: Raw data can be transformed and analyzed in a variety of ways, depending on the needs of the organization. This flexibility allows for more customized analytics and reporting.
Deeper insights: Working with raw data allows for deeper insights into system performance, security breaches, and other critical events. This is because raw data can provide more detailed information than parsed data.
More comprehensive coverage: Working with raw data can help organizations capture and analyze all available data, rather than just a subset of parsed data.
Which approach is best for syslog?
Deciding which approach to use depends on the needs of the organization. If real-time analytics and reduced storage costs are important, then parsing the data may be the best option. On the other hand, if flexibility and deep insights are more important, then working with the raw data may be the better choice.
Ultimately, it's important to have a solid understanding of the organization's needs and goals when working with syslog. By doing so, organizations can make more informed decisions about how to best work with their syslog data and achieve their desired outcomes.
Despite this, there may be some environments where the amount and complexity of data are so vast that the query response time may be too long. For these cases, the index logging will solve any performance issue. As a result of the indexing, you might not be able to drill down or follow different post-analysis troubleshooting. Nevertheless, if you already set the parameters that you want to analyze and correlate, index logging is the best method to get real-time results.
Using Viewtinet for syslog
The vision of Viewtinet has always been to create a flexible platform suitable for several and different use cases. Having said that, why limiting to one method when you can have both at the same time?
Viewtinet proposes an end-to-end solution. It is capable of integrating the data, store it and provide full analytics and observability. Thanks to the visual smart data broker, Viewtinet can integrate and parse syslog data from any vendor.
However, depending on the vendor and type of syslog data, or even if the company needs to keep the raw syslog data for regulatory reasons, Viewtinet can also work in this format. It has an intelligent layer to identify the different keys and we can select a key to identifiy the logs that content that key, and then filter by the available values.