Analyze data. Solve problems.
There is value in the machine data (logs and events) from your infrastructure and applications. However, storing and analyzing that data to extract that value can be a big (and expensive) undertaking for organizations. With log analytics, companies like yours can better understand your log data and take action to improve reliability and increase security.
Log files are produced by applications, operating systems, networks and other components of a technology stack. They comprise log messages and are stored for analysis with a log management system. Log data is often the most extensive data available for operations, application management or security analysis of the state of business systems.
So how can log analytics solutions help your log management?
What is log analytics?
Log analytics involves searching, analyzing, and visualizing machine data from your IT systems and technology infrastructure to gain operational insights. Log analytics includes reviewing event logs to identify bugs, reliability issues, security concerns and other risks. It can also find, analyze and visualize machine data produced by your IT systems, applications and infrastructure. With log analysis, you can actively monitor and assess application behavior, performance and any irregularities overall.
A baseline for the activity of aggregated and structured log data is provided by historical analytics, which may aid in troubleshooting when anything deviates from the set standards. Using information from analytics, DevOps personnel can create and highlight reliability metrics such as Service Level Indicators (SLIs) and Service Level Objectives (SLOs) to tie back to KPIs, SLAs and other essential business measurements. Log analysis also helps with capacity planning and lifecycle management by providing insights into data trends and growth rates of the resources running business applications.
Numerous advantages may be gained from log analysis, like keeping apps reliable and secure, but these benefits cannot be achieved if the methods for managing logs and analyzing log files are not optimized.
What are the four main functions of log analytics?
Log analysis tools manipulate data to help users organize and extract information from the logs. These are the most popular methods for log analysis:
Normalization
Normalization is a data management method that converts sections of a message to the same format. A normalization step should be included in the process of centralizing and indexing log data, where attributes from log entries across applications are standardized and represented in the same manner.
Pattern recognition
Log analysis software can construct machine learning programs that compare incoming messages to a pattern book and differentiate between "interesting" and "uninteresting" log entries. However, when an abnormal entry is found, such a system may issue an alert instead of disregarding ordinary log entries.
Classification and tagging
You might wish to aggregate log items of the same type as part of our log analysis and keep track of all mistakes of a specific kind across all apps. Or you might want to filter the information in various ways.
Correlation analysis
Correlation analysis is an analytical method that involves gathering log data from several systems and determining the log entries from each system that correspond to the known event. It is possible for an event to appear in logs from several sources after it has happened.
What are log analytics solutions?
Log analytics solutions are typically software solutions with data visualization and insights tools. These features help you find issues, analyze them, and fix them quickly and efficiently.
DevOps teams may consolidate log data from many apps and services using log analytics solutions. Once the data is consolidated in the log analytics solution, teams can design queries to quickly extract insights from the data. For real-time operational intelligence, log analysis use cases like performance analysis and forensics give a refined picture of your systems, apps, and log data. Using log analysis tools you can collect, filter and analyze data to monitor your applications with the aid of a log analyzer.
These log analysis tools let you contextualize the log data and identify opportunities for improvement. Organizations must be able to monitor hosts, processes, services, and applications in real-time for performance concerns as they transfer and manage their applications in the cloud. Developing a log analytics solution will give DevOps teams the ability to capture data and identify any concerns before they affect log management processes.
Sumo Logic: A comprehensive log analytics solution for your organization
With a solution like Sumo Logic, you can leverage your logs to:
Examine and address operational issues more quickly
Gather and correlate diverse types of machine data
Undertake proactive efforts such as capacity planning, patch status reporting and security audits
Reduce the mean-time-to-identification (MTTI) and mean-time-to-resolution (MTTR)
Capture and centralize all logs and metrics from your applications and tech stack
Scale and personalize a corporate intelligence platform for analytics and alerts
Learn more when you download the log analytics guide.
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