In the world of IoT, ensuring the reliability, efficiency, and security of connected devices is critical. As IoT devices generate massive amounts of data, detecting anomalies becomes increasingly important. Anomaly detection helps identify potential issues before they escalate, providing businesses with valuable insights and the ability to improve operational efficiency if used correctly. In this article, you will learn about some potential use cases for anomaly detection across different industries.

InfluxDB Clustered is an on-prem offering of InfluxDB 3.0, allowing you to deploy the newest version of InfluxDB on your own hardware and manage it with your team. With InfluxDB Clustered, you get high availability and performance out of the box and the ability to fine-tune InfluxDB to fit the performance requirements of your specific use case. InfluxDB Clustered is deployed and managed using Kubernetes.

Integrating data visualization tools with databases like InfluxDB is crucial for developers looking to enhance analytical capabilities and derive actionable insights from complex datasets. Grafana, Tableau, and Apache Superset—all of which you can use with InfluxDB—are popular visualization tools with different features and benefits.

InfluxDB is all about your data: we bridge the gap between an empty database bucket and business value and provide experts to help you derive value from your data. InfluxDB expert support teams come with contracted InfluxDB 3.0 serverless products (Serverless, Cloud Dedicated) and our Clustered on-prem product. Though no customer is left to figure everything out on their own, your product selection will determine the level of custom support you receive.

The Internet of Things (IoT) is rapidly reshaping the world. From smart devices in our homes to connected sensors in industrial settings, the amount of data generated is rapidly increasing. But what use is this data if we can’t collect and analyze it in real-time to gain key insights? This is where the MING stack (which includes Mosquitto/MQTT, InfluxDB, Node-RED, and Grafana) comes in. This powerful combination of open-source tools is intended to simplify IoT data management.

There’s so much talk about AI these days that it seems we quickly forget that AI isn’t a single type of technology. It’s a category, almost an umbrella term for a wide range of different technologies, applications, and approaches. The terms “Generative AI” and “Machine Learning AI” (often referred to as “Real-World AI”) describe two different branches that fall under the broader AI heading.

The Industrial Internet of Things (IIoT) has revolutionized the way industries operate, enabling businesses to collect and analyze data from their operations in real-time. However, managing and analyzing data from diverse sources can be a challenge. While sensors and systems may use the same transport protocols, the shape and type of data generated can vary from one device to another. A lack of uniform, clean data creates challenges and obstacles when it comes to getting timely insights.

To win the space race, aerospace and aviation companies must be fast. The end-to-end cycle of testing, visualizing test data, and making improvements demands swiftness, especially when a single launch yields billions of data points. It starts with real-time access to data. Real-time data analysis with nanosecond precision is crucial for monitoring environmental and habitat conditions when lives are at stake. Speeding up the iteration pipeline is essential but not sufficient. Cost efficiency matters too.

Wrapping up another quarter provides an ideal time to look back on the features we rolled out across various products. Software is never finished, and our engineers have been working hard to deliver improvements to InfluxDB 3.0. This roundup highlights some of the developments and releases over the last few months.

We’ve made the case many times that instrumentation is critical for understanding changes in the physical and virtual worlds. During this recent webinar, panelists discussed the challenges and opportunities of integrating IoT sensors into existing infrastructure, ensuring data quality and accuracy, and leveraging sensor data for operational efficiency and productivity.