{"id":5510,"date":"2022-02-16T16:13:42","date_gmt":"2022-02-16T16:13:42","guid":{"rendered":"http:\/\/www.odbms.org\/blog\/?p=5510"},"modified":"2022-02-16T16:13:43","modified_gmt":"2022-02-16T16:13:43","slug":"on-iot-and-influxdb-interview-with-paul-dix","status":"publish","type":"post","link":"https:\/\/www.odbms.org\/blog\/2022\/02\/on-iot-and-influxdb-interview-with-paul-dix\/","title":{"rendered":"On IoT and InfluxDB. Interview with Paul Dix"},"content":{"rendered":"\n

“Time is a critical context for understanding how things function. It serves as the digital history for businesses. When you think about institutional knowledge, that\u2019s not just bound up in people. Data is part of that knowledge base as well. So, when companies can capture, store and analyze that data in an effective way, it produces better results.<\/em>” –Paul Dix<\/strong>.<\/p><\/blockquote>\n\n\n\n

Q1. InfluxData just <\/strong>announced<\/strong><\/a> accelerated IoT momentum with new customers and product features. Tell us what makes InfluxDB so well-suited to manage IoT<\/a> data.<\/strong><\/p>\n\n\n\n

Paul Dix<\/strong>: We’re seeing time series data become vital for success in any industrial setting. The context of time is critical to understanding both historical and current performance. Being able to determine and anticipate trends over time helps companies drive improvements in mission-critical processes, making them more consistent, efficient and reliable. We built InfluxDB<\/a> to facilitate every step of this process. We\u2019ve been fortunate to work with several major players in the IIoT space already, so we\u2019ve been able to really understand the workflows and processes that drive industrial operations and better develop solutions around them. <\/p>\n\n\n\n

Q2<\/strong>. How do the new edge features for InfluxDB that you just announced help developers working with time series data for IoT and industrial settings?<\/strong><\/p>\n\n\n\n

Paul Dix<\/strong>: The new features give developers more flexibility and nimbleness in terms of architecture so that they can build more effective solutions on the edge that account for the resources they have available there. For example, we understand that some companies have very limited resources on the edge, so we\u2019ve made it easier to intelligently deploy configurable packages there. By breaking down the stack into smaller components, developers can reduce the amount of software they need to install and run on the edge. At the same time, we want developers to have the option to do more at the edge if they can. That\u2019s why we\u2019ve made it easier to run analytics on persistent data at the edge and to replicate data from an edge instance of InfluxDB to a cloud instance.<\/p>\n\n\n\n

We\u2019re also working to make it easier for IoT<\/a>\/IIoT<\/a> developers to manage the many devices that they need to deal with. One of our new updates allows developers to distribute processed data with custom payloads to thousands of devices all at once from a single script. On the other side of the equation, we have another new feature that helps contextualize IoT data generated from multiple sources, using Telegraf<\/a>, our open source collection agent, and MQTT topic parsing<\/a>.<\/p>\n\n\n\n

Q3<\/strong>. What makes time series data so important for IoT and IIoT? <\/strong><\/p>\n\n\n\n

Paul Dix<\/strong>: Time is a critical context for understanding how things function. It serves as the digital history for businesses. When you think about institutional knowledge, that\u2019s not just bound up in people. Data is part of that knowledge base as well. So, when companies can capture, store and analyze that data in an effective way, it produces better results. For example, manufacturers may want to know how long a valve has been in service, or how many parts their current configuration can produce per hour. Time is a constant measure that creates a baseline for comparative purposes, generates a current snapshot for systems and processes, and reveals a roadmap for identified patterns to persist and therefore become more predictable. <\/p>\n\n\n\n

Time series<\/a> data is well-suited to IoT and IIoT because it ties the readings from critical sensors and devices to the context of time. It\u2019s also easy to use persistent time series data for multiple, different purposes. We can think about temperature in this case. In a consumer IoT context, such as a home thermostat, users primarily want to know what the current temperature is. In an IIoT context, manufacturers want to know the current temperature, but also what the temperature was in the last batch, or the batch from the previous week. Using InfluxDB to collect and manage time series data makes these kinds of tasks easy. At InfluxData, we’re fortunate that InfluxDB is one of a select group of successful projects and products where IoT, data, and analytics deliver significant value to organizations and the customers they serve.<\/p>\n\n\n\n

Q4<\/strong>. Graphite Energy is featured in the announcement as a company that’s using InfluxDB to manage its time series data. Can you tell us more about the impact InfluxDB has had on its business?<\/strong><\/p>\n\n\n\n

Paul Dix<\/strong>: We\u2019re really excited about our work with Graphite Energy<\/a> \u2013 they\u2019re an Australian company that makes thermal energy storage (TES)<\/a> units. These devices get energy from renewable sources and store it until it\u2019s required for industrial processes in the form of heat or steam. Its goal is to decarbonize industrial production. <\/p>\n\n\n\n

All of Graphite Energy\u2019s operations are grounded in data \u2013 they\u2019re collecting time series data from their devices out in the field and use InfluxDB to store and analyze these millions of data points they\u2019re collecting daily. Graphite Energy uses that data to optimize its products, to guide remote operation, engineering and reporting, and to inform product development and research vectors. InfluxDB has also been a key component in the development of their Digital Twin<\/a> feature. For this, they use time series data to generate a real-time digital model of a TES unit, that is accurate to within five percent of actual device performance. This allows them to roll backward g and forward in time to track performance. The Digital Twin is a key component of the company\u2019s predictive toolkit and ongoing product optimization efforts. The more efficient Graphite Energy\u2019s TES units are, the better they\u2019re able to facilitate decarbonization. That\u2019s a win for everyone.<\/p>\n\n\n\n

Q5.<\/strong> How are some of your other IoT customers using the InfluxDB platform? <\/strong><\/p>\n\n\n\n

Paul Dix<\/strong>: Our customers are doing great things in the IoT space. I\u2019ll highlight just a few here quickly. <\/p>\n\n\n\n