Below is the list of blog posts with the “Log aggregation” tag.

Log collection tools gather event data from endpoints, servers, network devices, and cloud services, normalize it into a consistent format, and route it to a SIEM, a database, or long-term storage. This article compares five log collection tools: NXLog Platform, Splunk Universal Forwarder, Elastic Agent, Fluent Bit, and Vector. All five move logs reliably. The factors that determine a purchase are operating system coverage, where processing happens, which destinations each tool can feed to, and whether fleet management and storage are included or have to be assembled from separate parts.

Syslog is the standard protocol that devices, operating systems, and applications use to send event messages to a log collector. NXLog Platform collects, parses, and forwards messages in both BSD (RFC 3164) and IETF (RFC 5424) formats with a single extension. Most security telemetry still travels as syslog at some point in its life. Firewalls, switches, Linux hosts, hypervisors, and a long list of security products emit it, and every major SIEM ingests it.

When you reconstruct an incident across firewalls, endpoints, and application logs, the timestamps rarely agree. One source records Oct 10 13:55:36 with no year and no time zone. A Zeek log two racks over emits 1760104536.225121. The firewall already speaks 2025-10-10T13:55:36.003Z. Until every one of those is converted to a single format and time zone, your event timeline is an estimate. The fix is timestamp normalization: convert each timestamp to ISO 8601 in UTC at the point of collection, before the data reaches your SIEM.

Pick the wrong log collector and you pay for it on every node you deploy. A heavier agent multiplied across a thousand hosts is real memory and CPU you can’t get back, and a pipeline wired tightly to one vendor’s backend is hard to unwind later. So the Fluentd vs Logstash decision usually comes down to two questions: how much processing do you need at the collection point, and how committed are you to the Elastic Stack?

The choice here is not between two interchangeable log tools. It is a choice about where you want parsing, routing, and failure handling to live. Filebeat runs close to the source and keeps collection small. Logstash sits in the middle of the flow and takes on filtering, enrichment, and fan-out. That architectural difference matters more than a feature checklist. Pick the narrower tool when your logs have one destination and your parsing rules are modest.

Centralized logging is no longer optional. Whether you’re troubleshooting production incidents, investigating suspicious activity, or meeting audit requirements, you need a way to collect logs from many sources, normalize them, search them quickly, and turn them into alerts and dashboards. In practice, that starts with reliable collection — often via solutions like NXLog Platform — so the data arrives clean and consistent. Two of the most common open-source paths people compare are Graylog vs ELK Stack.

People think about logs as one of the biggest chores in the IT industry. Well, that does not necessarily need to be true. If you adhere to some fundamental log management best practices, the value you could get out of them quickly outweighs the effort put into managing them. Logs can easily become the best friend of IT teams looking to keep their systems secure, meet compliance requirements, and maintain a smoothly running network.

Logs play a critical role in IT infrastructure, and choosing the right log management solution is key to effective operations. This guide explores the core principles for selecting a solution that aligns with your log collection and management needs. Given the wide range of options available, we categorize them into three main groups for clarity. End-to-end Log Management Solutions Security Information & Event Management (SIEM) Application Performance Monitoring and Observability (APM)

Agentless log collection refers to gathering log data from various sources without installing dedicated software agents on the systems generating the logs. Instead, it leverages protocols such as SNMP traps, WECS, WMI, and syslog to retrieve log data remotely. It is easier to explain what agentless log collection is by also providing some context about agent-based log collection. The truth is that these two options for collecting logs walk hand in hand, meaning that they can and will likely coexist on your network.

Logs are a record of the internal workings of a system. Nowadays, organizations can have hundreds and, more regularly, thousands of managed computers, servers, mobile devices, and applications; even refrigerators are generating logs in this Internet of Things era. The result is the production of terabytes of log data—​event logs, network flow logs, and application logs, to name a few—​that must be carefully sorted, analyzed, and stored. Without a log management tool, you would need to manually search through many directories of log files on each system to access and extract meaning from these millions of event logs.

Apple has made great strides in recent years, not only with its innovative hardware, but also with incremental improvements to its operating systems. For a number of reasons, Macs have become viable alternatives to PCs in many large corporations. Apple also continues to maintain a strong presence in institutions of higher education, as it has for decades in the US. Whether your Mac users are working on spreadsheets in accounting or they belong to creative teams developing software or marketing content, your digital assets are valuable and need to be monitored to detect any potential security threats.

The value of log aggregation There is no denying the importance of log aggregation for multi-million-dollar enterprises worldwide. But just what is log aggregation? And how can it help your organization? Well, log aggregation is the process of standardizing and consolidating your log data from distributed systems across your network into one centralized server. By doing so, you have a unified view of what occurs across your entire IT infrastructure.

In this post, we will look at how to use Raijin Database Engine as a backend in a centralized logging environment for collecting and aggregating Windows security events. We will also show you how to integrate Raijin with an open source data exploration tool. Finally, you will see how you can track suspicious network activity and identify specific types of intrusion on Windows hosts using these tools. A low-cost, lean and mean data discovery solution Although the combination of tools we present here cannot compete with a full-fledged SIEM solution, they do offer quite a few advantages for security analysts who need a responsive, highly customizable data discovery solution that accepts ad hoc SQL.

IT security should be one of the main focus points of all enterprises. In today’s world, when digital transformation is taking place at an unprecedented pace, securing online data is vital for all kinds of businesses. This is why most companies are utilizing SIEM (Security Information and Event Management) solutions that help them identify threats before they can do any harm. Even though SIEM tools are perfect for event correlation and analytics, it is not part of their core functionality to manage log collection, filtering, distribution, and formatting.