People’s involvement has been inevitable with log management despite advancements in ITOps. Log management at a high level collects and indexes all your application and system log files so that you can search through them quickly. It also lets you define rules based on log patterns so that you can get alerts when an anomaly occurs.
Log management analytics solution leveraging RDA has been able to detect anomalies and aid predictive models over a machine learning layer. This has demonstrated improved efficiencies and a direct reduction in costs.
How does a conventional log management system work?
To make things better, log analytics has been deployed. However, log analytics alone cannot replace human interactions. Log analytics systems produce a significant increase in alerts which trigger the need for more support from IT personnel, thus limiting its progress anytime soon. There is still a need to blend it with traditional operations practices, namely on-call monitoring teams and consulting analyst teams who respond to any alert received by the system.
However, none of these mechanisms could explain or ease up the burden of an engineer sitting at his workstation while he/she responds to every notification generated by the log monitoring tools without being able to pre-determine whether such notifications are critical or not.
Challenges of a conventional log management tool
A log management tool has to be fast. Without this, users will not be able to search for logs. It also has to have enough storage so that it can index logs from an entire enterprise. This can be very expensive depending on the number of servers or applications you need information from.
A log management tool needs to support every platform (Windows, Linux, Unix) your business uses in order to pull relevant metrics and log data you need for troubleshooting and analysis.
In a conventional way, automating aggregation and distributing to different systems is not possible.
RDA-enhanced log management system
With Robotic Data Automation(RDA), your log management system will be augmented with an intelligent layer to increase efficiency without disrupting any established processes. RDA also enables the creation of advanced ML-based dynamic baselining and predictive models that makes anomaly detection efficient and more reliable.
The benefits of using an RDA-enabled log management solution for your business include reduced costs and improved efficiencies in managing logs.
- Automates alerts: The first benefit is that RDA automates the alert generation process from manual rules-based. It analyzes the raw log data and dynamically learns alert conditions by minimizing the risk of missing alerts from new/modified log entries.
- Auto-correlation of alerts: It supports intelligent alerting capabilities with auto-correlation of alerts across different systems/services, resulting in a reduced number of alerts turning into trouble tickets.
- Contextual Insights: It automatically provides contextual log data when analyzing the trouble ticket thereby reducing the need for users to manually search logs
- Removes duplicates: It prevents unnecessary alert noise by removing duplicates across various systems while increasing the overall quality of alerts and insights derived from them.
- Alert log pattern summary: It automatically summarizes the alert log patterns as time-series data making it efficient and cost-effective to retain historical knowledge.
- Builds regression pipeline: It allows building a regression pipeline on any attribute for predicting the trend and anomalies.
- Brings new data sources: RDA also helps in bringing new data sources into existing log management systems where native methods are either complex or simply do not exist.
RDA offers out-of-box integration with Splunk and Elastic search products. Combining RDA with Splunk/ELK not only significantly reduces the overall efforts required for managing IT Operations but also provides an excellent set of capabilities for detecting anomalies in near real-time. Also, using RDA reduces false positives, meaning alerts generated are not a result of false or random events.
By building these capabilities on top of existing log management analytics investments, organizations and IT Ops can reduce costs while improving efficiency and business outcomes.
RDA vs AIOps
If you are looking at optimizing the operations of a single or small subset of IT tools like a log management analytics platform, RDA can be a better choice. Standalone RDA is faster to deploy/adopt and validate the benefits at a lower cost.
AIOps, on the other hand, is designed to bring transformation across IT functions. It integrates with multiple data sources and performs cross-domain correlation and analysis to deliver actionable insights.
RDA with CloudFabrix
RDA is also provided as a standalone tool and as a hosted offering to help iterate, author, and publish data workflows.
So with CloudFabrix, organizations can start with RDA, validate the technology promise, and seamlessly upgrade to AIOps, delivering the value across functions.