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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.