August 2016 Summaries

Logz.io's Cognitive Insights is a feature that enhances log analysis by integrating machine learning and crowdsourcing to identify potential issues within IT environments before they impact business operations. Built on the UMI™ artificial intelligence engine, Cognitive Insights analyzes log data through various data sources like social threads and open-source repositories to expose critical events that might otherwise go unnoticed. This tool is exemplified with a Java application setup where SQL syntax errors are detected and correlated with online resources, facilitating quick resolution. Users can interact with insights in the Logz.io interface, providing feedback to refine the UMI engine’s accuracy over time. This feature, embedded within the ELK Stack, offers a proactive approach to managing system health by surfacing actionable data directly in the Kibana dashboard, thereby leveraging community knowledge to enhance observability and prevent system disruptions.

Logz.io has launched its Cognitive Insights platform, which utilizes artificial intelligence to identify important log data, enhancing the analytics capabilities provided by its existing enterprise-grade cloud service that integrates with the open-source Kibana interface. The platform is built on Unified Machine Intelligence (UMI), a technology developed to mimic human troubleshooting processes, leveraging supervised machine-learning algorithms to analyze interactions with log data and social resources, akin to Google's PageRank and Amazon's recommendations. This approach aims to reduce the time and effort spent on troubleshooting by highlighting critical events within log data, potentially even predicting future issues. The release of Cognitive Insights marks a significant step in Logz.io's mission to help more than 1,000 companies efficiently analyze and interpret log data, with plans to expand its features and applications throughout the year. Existing users have immediate access to this new functionality, while others are invited to explore it through a free demo account.

Windows event logs provide crucial information for monitoring and analyzing Windows environments, especially in system administration and security compliance contexts. However, managing the vast amounts of data generated can be challenging, which is where the ELK Stack—comprising Elasticsearch, Logstash, and Kibana—proves advantageous. Winlogbeat, part of Elastic’s Beats product line, facilitates the shipping of Windows event logs to the ELK Stack, allowing for efficient data ingestion and analysis. The configuration process involves setting up Winlogbeat to track specific event logs, filter them based on user-defined criteria, and forward them to Elasticsearch or Logstash. This setup enhances the ability to visualize and analyze logs using Kibana’s dashboards, which can be customized to suit different needs. The integration of ELK with Windows is increasingly popular due to its flexibility and open-source nature, offering features that are not available in traditional solutions, such as powerful querying capabilities and the creation of detailed visualizations.