With the increased adoption of the Industrial Internet of Things (IIoT), connected devices and sensors generate vast amounts of data, and you’ll need an effective way to capture, store, and visualize all of it. With effective data visualization and analysis, you can transform raw data into actionable insights and make informed decisions. This post will break down tools like Grafana, Node-RED, and time series databases, including their benefits to your IIoT workload.

We recently adjusted how we handle “partial writes” with our InfluxDB Cloud Serverless product using the v2 Write API. This only applies to InfluxDB Cloud Serverless customers (those who created their Cloud accounts after January 31, 2023). In the near future, we will make this change for InfluxDB Cloud Dedicated and InfluxDB Clustered customers as well.

I run a small Kubernetes cluster at home, which I originally set up as somewhere to experiment. Because it started as a playground, I never bothered to set up monitoring. However, as time passed, I’ve ended up dropping more production-esque workloads onto it, so I decided I should probably put some observability in place. Not having visibility into the cluster was actually a little odd, considering that even my fish tank can page me.

The state of industries has come a long way since the Industrial Revolution with new technologies such as smart devices, the internet, and the cloud. The Industrial Internet of Things (IIoT) is a network of industrial components that share and process data to gain insights. But as IIoT involves sensitive data and life-critical operations, this also comes with various IIoT cloud security challenges. Therefore, it is important to strengthen security.

With over 8 billion smartphones in use, predominantly running Android, how do you efficiently manage and analyze the flood of real-time data generated by apps, games, and other services? Whether it’s tracking user interactions, monitoring health metrics, or managing IoT devices, handling this data can be overwhelming.

Enterprise Java developers understand the frustration of sluggish application performance in production. Diagnosing issues within complex microservice architectures can be a time-consuming nightmare. Thankfully, the popular Java framework SpringBoot provides a robust observability stack to simplify real-time monitoring and analysis. By harnessing the power of libraries and tools such as SpringBoot Actuator, Micrometer with InfluxDB, and Grafana, you can gather meaningful insights easily and quickly.

New capabilities, including faster query performance and management tooling, advance the InfluxDB 3.0 product line InfluxDB Clustered general availability gives developers the power of InfluxDB 3.0 for the self-managed stack.

Over the past year since its initial release, the InfluxDB 3.0 product suite has seen numerous new features and performance improvements. These improvements reinforce InfluxDB 3.0’s position as the industry’s leading time series database, offering unparalleled performance with unlimited cardinality, high-speed, independently scalable ingest, real-time querying, and superior data compression using Parquet format on cost-effective object storage.

In a previous post, we described a CSTR and a PID controller. This post will cover the code and architecture of the digital twin from this project repo. The project leverages Kafka for data streaming, Faust for data processing, InfluxDB for storing the time series data, and Telegraf for writing data from the topic to InfluxDB. We’ll also cover the advantages and disadvantages of this stack.

In the fast-evolving chemical industry, maintaining precise control over Chemical reactors like a continuous stirred-tank reactor (CSTRs) is paramount to ensuring optimal performance and product quality. This blog post delves into integrating advanced data tools and techniques to achieve this control. We’ll explore how to leverage InfluxDB, Kafka, and Faust streaming, along with Telegraf, to effectively model and manage a CSTR and its PID controller (Proportional-Integral-Derivative Controller).