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GrafanaCon Recap: Running a Power Plant with Grafana

A water and energy innovation company founded in 2005, Natel Energy builds hydropower turbines and designs resilient and distributed hydropower systems. In his talk at GrafanaCon EU, Natel Developer Ryan McKinley gave us a fascinating look at how the company is using Grafana to help run these next-generation power plants.

Use New Range Markers to Show The Duration of a State Change

In our world of distributed systems, state changes to your infrastructure often take some time to propagate. With a few exceptions (for example, feature flags), single point in time changes are rare. Deploys, outages, database migrations, failovers, stress tests; none of these things are instantaneous – all have some duration during which the system is changing.

Fishing for Log Events with Graylog Sidecar

Getting the right information at the right time can be a difficult task in large corporate IT infrastructures. Whether you are dealing with a security issue or an operational outage, the right data is key to prevent further breakdowns. With central log management, security analysts or IT operators have a single place to access server log data. But what happens if the one log file that is urgently needed is not collected by the system?

Logz.io Available on the AWS Marketplace

The AWS marketplace gives users access to a large variety of SaaS and API products that can be easily found, subscribed to and used. One of the biggest advantages for users is the ability to benefit from an easier procurement and payment process — users can use their existing paying terms with AWS, and unify billing across all the AWS services they are consuming, including those offered in the marketplace. Logz.io is now available on the AWS SaaS Marketplace.

SCIPY STACK VS. INFLUXDB AND GRAFANA

Scientific python programmers adore Pandas due to its many functionalities. In particular, for data manipulation and analysis it offers handy data structures and operations for numerical tables and time series. Combined with the rest of the SciPy stack and scikit-learn (e.g. for Machine Learning Analysis), multiple goals can be achieved. When it comes to on-line data analysis, interaction, or simple data navigation by multiple users, the SciPy stack can be stressed to its limits.