As the amount of telemetry being collected continues to grow exponentially, businesses are continuously seeking cost-effective ways to monitor and analyze their systems. Data collection and monitoring can be expensive, especially when dealing with large volumes of logs. One approach to maintaining visibility while reducing the amount of data collected is through creating log-based metrics.

In modern business environments, where everything is fast-paced and data-centric, companies need to be able to track and analyze data quickly and efficiently to stay competitive. Metrics play a crucial role in this, providing valuable insights into product performance, user behavior, and system health. By tracking metrics, companies can make data-driven decisions to improve their product and grow their business.

In the previous blog in our root cause analysis with logs series, we explored how to analyze logs in Elastic Observability with Elastic’s anomaly detection and log categorization capabilities. Elastic’s platform enables you to get started on machine learning (ML) quickly. You don’t need to have a data science team or design a system architecture. Additionally, there’s no need to move data to a third-party framework for model training.

Technology drives so many aspects of our lives in today’s fast paced world. Keeping up with how technology helps us in our work and personal lives is challenging and it's sometimes unclear as to how to go about learning new technology skills to drive business resilience across an organization.

As fraud continues to grow in prevalence, SecOps teams are increasingly investing in fraud prevention capabilities to protect themselves and their customers. One approach that’s proved reliable is the use of log analytics and telemetry data for fraud prevention. By collecting and analyzing data from various sources, including server logs, network traffic, and user behavior, enterprise SecOps teams can identify patterns and anomalies in real time that may indicate fraudulent activity.

We’ve just recently completed KubeCon + CloudNativeCon Europe 2023 in Amsterdam, one of the signature events of the year in the cloud native, open source and observability spaces. I was thrilled to be joined by Taylor Dolezal, the Head of Ecosystem for the hosting Cloud Native Computing Foundation (CNCF) to discuss the insider happenings of KubeCon EU live from Amsterdam for the April 2023 OpenObservability Talks podcast.

Since day one, Cribl has been on a mission to give users more control and more value from their observability and security data. We had a feeling that putting customers first would be the key to unlocking that value, so “Customers First, Always” went at the top of the list when the time came to talk about company values.

Mezmo Telemetry Pipeline helps organizations Ingest, transform, and route telemetry data to control costs and drive actionability. Modern organizations are adopting telemetry pipelines to manage challenges with telemetry data (logs, metrics, traces, events) growth and to get the most value from their data investments.

As data science and data analytics become integral to our everyday lives, the demand for certifications in this field is at an all-time high. There are many different paths to pursue a certification in these areas — still, some stand out above the rest. In this blog post, we will explore the top 5 data science and analytics certifications that are essential for anyone looking to build a strong career in this field.