Today, we’re excited to announce InfluxDB Clustered, our latest product developed on the InfluxDB 3.0 product suite. InfluxDB Clustered is the evolution of InfluxDB Enterprise, our popular self-managed product for large-scale time series workloads. For enterprises, the performance leap from InfluxDB Enterprise to InfluxDB Clustered is orders of magnitude higher with significant improvements across analytics, storage, and costs.

SAN FRANCISCO – September 6, 2023 – InfluxData, creator of the leading time series platform InfluxDB, today announced InfluxDB Clustered, its self-managed time series database for on-premises or private cloud deployments. With the release of InfluxDB Clustered, InfluxData completes its commercial product line developed on InfluxDB 3.0, its rebuilt database engine optimized for real-time analytics with higher performance, unlimited cardinality, and SQL support.

Predictive analytics harnesses the power of big data, statistical algorithms and machine learning techniques to anticipate future outcomes based on historical data. Various industries use predictive analytics, from finance and healthcare to retail and marketing. Among its many uses, predictive maintenance and anomaly detection are two significant applications.

Self-hosting is effective for many companies. But when is it time to let go and try the easier way? There’s no such thing as free lunch, or in this case, free software. It’s a myth. Paul Vixie, vice president of security at Amazon Web Services, creator of the original Domain Name System (DNS), gave a compelling presentation at Open Source Summit Europe 2022 about this topic.

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.