As your data continues to grow and time remains critical, making data-driven decisions has never been more important (and let’s face it, that’s no small feat). Luckily, our new Scheduled Reporting feature is here to help—automatically delivering your logs, metrics, and tracing data in visually-rich custom dashboards, exactly when you need them, directly to the inboxes of your chosen recipients.

As organizations modernize their applications and deliver more complex, cloud-based services, the traditional boundaries between DevOps, SecOps, and ITOps are disappearing. Seamless collaboration between these teams, often referred to as DevSecOps, has become essential for efficiently addressing both reliability and security challenges.

Logs play a critical role in monitoring your applications and systems in terms of health, system behavior, and problem diagnosis. However, logs can assuredly bring value only if they are structured and well-formatted. Effective log formatting can help identify an issue to fix on time rather than having to sift through unorganized, hard-to-read logs. In this blog, we delve into 7 super-effective practices for production logging to help you maximize your log analysis capabilities.

In today’s complex environments such as cloud-native technologies, containers, and microservices-based architectures, reliable log monitoring is crucial for keeping your systems secure and resilient. Continuous monitoring enables organizations to stay in-control, providing proactive insights into system health and performance. With platforms like AWS, GCP, and Azure churning out massive amounts of logs, it’s easy to get overwhelmed.

In today’s fast-paced digital landscape, enterprise data stands as both a critical asset and a potential liability. With data volumes expanding at an annual rate of 28% while budgets increase by only 7%, organizations face mounting challenges. The unpredictable nature of data value complicates decisions on what to store and where. Moreover, the rise of connected devices and evolving security threats further exacerbate the situation.

Since the release of ChatGPT in November 2022, companies have either banned or rushed to adopt generative artificial intelligence (GenAI), which is rapidly expanding in use and capabilities. Its powerful yet unpredictable nature poses significant cybersecurity risks, transforming it into a double-edged sword. However, whether generative AI poses more opportunity or risk is not necessarily the right question to ask.

With the upcoming release of JDK 23, there are some significant changes in locale information that will affect Elasticsearch and how you ingest and format datetime data. Firstly, a bit of background.

Telemetry data, at its core, is all about transmitting real-time information from remote sources to centralized systems for analysis and action. This data is super important across different industries due to its ability to provide immediate, actionable insights that enhance operations and strategic decision-making.