Top Trending Java Anomaly Detection Tools to Know Application failures occur due to a wide array of reasons, and there are several tools available to address possible sources for errors including log management tools, error trackers, and performance monitoring solutions. In this blog, we’ve researched and found the different ways to solve Java application errors and how tools can assist in identifying errors. The data that these tools collect is often comprise of unnecessary elements. How to know its relevance? That’s where anomaly detection tools come into picture. These tools have so much importance for the Java Application Development Company to attain desired results for the business. In the following post we’ll focus on detecting and predicting when anomalies happen. Let’s check them out.
1. X-Pack X-Pack is basically an extension to the ELK Stack that uses algorithms to help users know the behavior of their logs, identifying when they’re not working as desired. The package depends on logs as its data source, allowing the users to know how specific metrics have its impact on the application and how users experience it. Features: • Finding anomalies inside Elasticsearch log data as well as metrics.
• Finding security threats by monitoring network activity as well as user behavior. • Finding log events causing anomaly. 2. Loom Systems Loom Systems provides analytics platform to find out anomaly in logs and metrics. It checks anomalies in logs, and also offers anomaly detection inside the operational analytics. Features: • Automated log parsing and analysis from multiple applications. • Suggested resolutions depending on the solution database. • Business operation anomaly detection. 3. OverOps OverOps shares information regarding when, where and why code breaks in the production phase. This tool gives user the complete source code and variable state across the complete call stack for each error, and lets you simply identify when new errors are triggered into the application. Features: • • • • •
Full code and variable state visibility to by default reproduce any error. Proper detection of all new and critical threats by code release. Native Java agent that doesn’t depend on log files. Working with aStatsD complaint tool for anomaly detection visualization. No code and configuration customization, installs through SaaS, Hybrid, and On-Premises • A perfect dashboard available with a dark theme. 4. Coralogix Coralogix groups and finds the similarities present in the log data. The tool works on common flows, identifying the log messages that are related to them, and notifying when an action didn’t cause the desired outcome. Features:
• Groups and summarize logs having the same pattern. • Finding of connected actions, and identifying of anomalies present. • Specifying anomalies that only present after a new version of the web application was deployed. 5. Anodot Anodot provides an anomaly detection system with the essential analytics for the users. Their focus is to identify anomalies in databases of any type, along with finding anomalies in business related information. Features: • Behavioral correlation and grouping of similar logs. • Identification of business data anomalies within marketing campaigns, clicks and performance indicators. • Reduce noise by grouping similar anomalies into one alert. Wrapping Up: Anomaly detection is highly beneficial to gain better insights of Java web applications. Each tool has its own style of working to identify anomalies within the web application. The most essential thing we should know is that it’s not only about the interface; it’s about the data. That’s why it becomes crucial to hire Java developer India to explore each one, and take final decision on selecting tools that give best value as per the problem that you’re trying to address. Developers will help you to implement the best tool for the web application.