Cloud computing, mobile devices, and IoT technologies continue to evolve and proliferate. As a result, businesses are generating and collecting more data than ever before. Data is generated and stored every time a customer interacts with a website or device. Savvy companies understand the importance of capitalizing on that data. It enables them to enhance customer experiences and increase profitability, among other countless benefits.

Regular expressions can be crucial for wrangling log data efficiently. You may want to extract specific data from your logs to make it easier to analyze and visualize. Sometimes you might want to capture an email when a particular message is logged. Other times, you may find yourself needing to hide sensitive data in logs before they are saved. And more often than not, you need to match using a RegEx pattern rather than an exact text search. This guide is split into three parts.

Using percentages when performing data analytics is an essential approach to effective numeric comparison, especially when the data in question demonstrates drastically different sample sizes or totals. Percentages allow for a quick and accurate understanding of how much data sums have changed across a dimensional category like a range of time, geographic regions, product lines, etc.

At AWS re:Invent recently, we excitedly announced Logz.io Infrastructure Monitoring – our new Grafana-based monitoring product! This product is the third pillar of our cloud observability platform, together with Logz.io Log Management and Logz.io Cloud SIEM.

Any system connected to the Internet is vulnerable to malicious attacks and breaches. If it’s online, there’s someone out there trying to break into it and do something bad with it (usually stealing data). Plain and simple. To protect your most valuable assets, you need bulletproof security measures, a skilled SecOps team, robust investigation tools, and reliable prevention/mitigation strategies.

Before the need for log correlation, there was a time not so long ago when reading software application logs was simple. Your application would output log files in sequential order, and you’d read through them. In the event of a bug, software outage, or security incident, you could easily parse what happened and when. It was a tedious process, but it was simple.

Following the fully featured public beta of the Elasticsearch Service on Microsoft Azure earlier this year, we are pleased to declare it is now generally available! Existing Elasticsearch Service customers can log in and launch deployments on Azure in their existing accounts, and new users can get started with a free 14-day trial of the Elasticsearch Service.

Kubernetes has become the de-facto solution for container orchestration. While it has, in some ways, simplified the management and deployment of your distributed applications and services, it has also introduced new levels of complexity. When maintaining a Kubernetes cluster, one must be mindful of all the different abstractions in its ecosystem and how the various pieces and layers interact with each other in order to avoid failed deployments, resource exhaustion, and application crashes.

Behavior analytics is a powerful tool in intrusion detection and prevention, using insights from typical user behavior to detect suspicious activity. What role does log monitoring have in this process and how does it work together with behavioral analytics?