Combining these two platforms provides an efficient, scalable and customizable tool for real-time data monitoring and alerting. In the data analytics and visualization world, it is crucial to have a system that not only effectively monitors your data, but can also alert you about any potential discrepancies or anomalies that may arise. One powerful tool set that enables you to monitor and alert on time series data is Grafana and InfluxDB Cloud Serverless.

Earlier this year, I had the pleasure of speaking at the Open Source Summit North America. When choosing a topic, I felt it was time to return to our roots and discuss the subject that originally put InfluxDB on the map: infrastructure monitoring. What was especially exciting was the opportunity to showcase the new capabilities of InfluxDB 3.0 to the open source community and explain their significance for the future of infrastructure monitoring use cases.

In today’s fast-paced world, applications are vital for driving businesses forward. However, without proper monitoring and insights into your application’s performance, you can’t identify what causes slow response times, high CPU usage, or database bottlenecks. But with an Application Performance Monitoring (APM) tool, you can gain deep visibility into your application’s performance by tracking critical metrics.

The TensorFlow framework, developed by Google, is one of the most important platforms for creating deep neural network research and for training machine learning algorithms. Generally, it runs on the server side using powerful GPUs, consuming power and a lot of memory.

This article, written by Shan Desai, was originally published on his blog and is reposted here with permission. Shan is a Software engineer currently employed at Emerson Discrete Automation and is an Open-Source Contributor / DIY Tech Enthusiast currently working with Industrial IoT. Telegraf is widely used as a metric aggregation tool thanks to the diverse number of plugins it provides that interface with a multitude of systems without having to write complex software logic.

A common question we get asked is “what client library should I use with InfluxDB 3.0?” This question isn’t as simple as it may seem. It can get confusing when deciding which client library to use while developing applications to write to and query from InfluxDB. There are numerous options to choose from and the answer may differ based on the following criteria: At first, this seems like an easy answer.

Teréga, a gas storage and transportation company in southwest France, manages a network of 5,000 kilometers of natural gas pipelines. The company’s mission is to accelerate the energy transition currently taking place, both at a territorial and a European level. It aims to extend a culture of responsibility to all its business and day-to-day activities.

In today’s data-driven world, the ability to collect and query data from multiple sources has become a very important consideration. With the rise of IoT, cloud computing and distributed systems, organizations face the challenge of handling diverse data streams effectively. It’s common to have multiple databases/data storage options for that data. For many large companies, the days of storing everything in the singular database are in the past.

With the release of InfluxDB 3.0, one of the big questions is: how does it compare to previous versions of InfluxDB? We have begun benchmarking InfluxDB 3.0 with production workloads to start giving users more insight into the benefits of adopting InfluxDB 3.0. In this post, we look at recent benchmarks comparing InfluxDB 3.0 to InfluxDB Open Source (OSS) 1.8.

We want machines in good working order, making products of superior quality. This isn’t news. But what is newsworthy is that routine maintenance can still lead to more downtime than necessary. Not all maintenance programs are created equally. Keeping capital equipment running doesn’t exist inside a vacuum of chance. Outside the fraction of unavoidable catastrophes, there’s much power in the decision-making process.

Grouped aggregations are a core part of any analytic tool, creating understandable summaries of huge data volumes. Apache Arrow DataFusion’s parallel aggregation capability is 2-3x faster in version 28.0.0 for queries with a large number (10,000 or more) of groups.