Advanced Manufacturing Monitoring Using ctrlXOS from Bosch Rexroth with InfluxDB
Session Date: Dec 09, 2024
Time: 2:00pm (PT) | 10:00pm (GMT)
Industrial manufacturers face increasing pressure to optimize operations, reduce downtime, and make data-driven decisions in real-time. This technical webinar demonstrates how the powerful combination of ctrlXOS from Rexroth and InfluxDB’s time series platform creates a robust Industrial IoT monitoring solution that addresses these critical challenges regardless of the environment (at the edge, on-prem or in the cloud).
Join us to discover how this integration enables standard and real-time monitoring capabilities through Node-RED and Telegraf, providing manufacturers unprecedented visibility into their operations. We will showcase practical implementations for both local and remote monitoring scenarios, making this solution accessible for various manufacturing environments.
Key Topics:
- Current challenges in Industrial IoT and how ctrlXOS + InfluxDB solves them
- Step-by-step integration guide, including installation and library setup
- Live demonstration of standard monitoring through Node-RED and Telegraf
- Real-time monitoring capabilities using Telegraf server agent
Watch the Webinar
Watch the webinar “Advanced Manufacturing Monitoring Using ctrlXOS from Bosch Rexroth with InfluxDB” by filling out the form and clicking on the Watch Webinar button on the right. This will open the recording.
[et_pb_toggle _builder_version=”3.17.6” title=”Transcript” title_font_size=”26” border_width_all=”0px” border_width_bottom=”1px” module_class=”transcript-toggle” closed_toggle_background_color=”rgba(255,255,255,0)”]
Here is an unedited transcript of the webinar “Advanced Manufacturing Monitoring Using ctrlXOS from Bosch Rexroth with InfluxDB.” This is provided for those who prefer to read than watch the webinar. Please note that the transcript is raw. We apologize for any transcribing errors. Speakers:
- Mauro Riboni: Product Manager, Rexroth
BAILEY MELLOS: 00:04
Hi, everyone. Thank you for coming. We’ll get started in just a minute. If you are already here, we would love to see where everyone’s tuning in from. If you want to drop your name and your location in the chat. My name is Bailey, and I’m tuning in from San Diego, California. Mauro, where are you at? Tell the crowd.
MAURO RIBONI: 00:45
I’m actually in Italy. Yeah. Okay. So—
BAILEY MELLOS: 00:53
All right. Yeah. We can go ahead and get started.
MAURO RIBONI: 00:56
Yeah.
BAILEY MELLOS: 00:57
So, thank you, everyone, for joining us today. Before getting started, I just want to remind you that the webinar is going to be recorded, and it’ll be available on demand within the next 24 hours, and the slides will be included in that as well. If you have any questions at all, you can use the Q&A section throughout the session. It’s at the bottom of your screen, and we’ll answer these towards the end of the webinar. Don’t forget to check out InfluxDB Community Slack workspace as well in the community forums. We have a lot of Influxers and other community members in there answering people’s questions, and it’s a fun place to go and hear what’s going on. So that said, my name is Bailey. I work at InfluxDB, and I’m here today with Mauro Riboni, who’s going to talk to us today on our webinar, “Advanced Manufacturing Monitoring Using ctrlX OS from Bosch Rexroth with InfluxDB.” So, I’ll pass it over to you, Mauro.
MAURO RIBONI: 01:48
Okay. So, I share my screen, and I start presenting. So do you see my screen?
BAILEY MELLOS: 01:57
Yeah, we can see.
MAURO RIBONI: 01:58
Okay. Perfect. So before starting, I want to introduce myself. I’m Mauro Riboni. I started to work in Rexroth as an application engineer. So, I was a real technical guy. And I started working first in Italy, then I moved to Germany as a second-order support engineer. And now I cover in the position of product manager for ctrlX OS IoT applications. And that’s why I’m here to present to you and show how InfluxDB is working with ctrlX OS. So, let’s first see the agenda. So, we are starting to introduce Rexroth. What is Rexroth? So, it’s probably the first question you have. So, Rexroth and InfluxDB, why it’s good to use them together. And then we are going to see some use case overview and implementation. So standard monitoring and real-time monitoring, which are the two main use cases we have in mind when using InfluxDB over ctrlX OS. And then in the end, we end with the Q&A sessions.
MAURO RIBONI: 03:11
So, let’s start. So, what is Rexroth? Rexroth is a Bosch company and is really focused on the industrial world. And our focus is industrial hydraulic, mobile hydraulic, automation, in general, electrical automation, linear technology, and robotic. But since 2019, we decided in our automation section to go in a new direction and have a new approach to automation because, yeah, we wanted something more future-oriented. We wanted to shape something which was more app-based and create really a ctrlX OS store, an app store for automation applications. And this kind of approach has driven us directly into 2022, where we decided to release our ctrlX OS. So basically, whatever things you are going to see today are possible to be achieved with our devices. So, our hardware that will be presented today. But many producers, many controllers, producers are going to use our operating system in their devices. Why? Because it’s secure. We maintain it. It’s always up to date. And yeah, it’s basically taking a standard technology and a readable technology to many controllers.
MAURO RIBONI: 04:54
So why Rexroth and InfluxDB? So, we have mainly in mind the two use cases. Monitoring, general purpose, which is good for energy monitoring, simple production data, and preventive maintenance for small dynamics. And in case the customer needs something stronger, we have the possibility to have a real-time monitoring. So, we can automatically sample at one millisecond, two milliseconds, depending on the clock time data and store them inside InfluxDB. It’s very important for production data for fast machines. Energy monitoring should be detailed and is important to get all the data and not skip measurements of current whatsoever. And preventive maintenance when we have to sample currents, torque, and high dynamics during operations. So, we can scale these kind of use cases on three levels. So, what we are seeing today can be seen as on edge, so implementing on edge. So, in this case, we have a ctrlX CORE with InfluxDB directly installed over ctrlX OS. We can see that also in plant levels. So, a machine can send the data not just to his own InfluxDB, but InfluxDB can be also directly installed inside the plant inside the factory itself and on cloud. So yeah, you can take the data in the same way, send them to the cloud in case ctrlX OS is connected to internet, then we have also this possibility.
MAURO RIBONI: 06:54
So, our ecosystem. Today is all based about ctrlX OS and InfluxDB, but we are— Before we start, I wanted to understand and show first our ecosystem and how our architecture is done. So, we have our ctrlX OS, which is running over our controller. Here on the left, we have our ctrlX CORE, which is basically our entry-level device and has three ports. Usually, the first port is a normal ethernet port. We have a central port where we can connect machines with ETR as a fill bus. And we have another port where we can use as the first port, a normal ethernet port, or implement all the protocols over. And inside the device, we can use different kind of application that can come from us, basically. They can come from our ctrlX World, which is our store where we can find the application created and used by our partners that are just sending the application in our store and our customers that can decide to buy from them using our store. But in the end, since we are open ecosystem, a customer can also decide to build his own application using our SDK. But of course, it’s not a ready product in this case, but the customer has to program.
MAURO RIBONI: 08:38
All the applications can talk using a data layer. We will see later what it is. It’s basically a data structure that we can use to store the data locally on the device, operative data, so it’s not a database. We can store temperatures, anything that is happening in the CORE. Everything is stored in the data layer, and different applications can decide to get the data and send and use them in order to share the information. So why is that important, the CORE, and why is it that important for InfluxDB? Our ctrlX CORE that works with ctrlX OS is the perfect match as we think, as we design it to join and to empower both the world from the machine’s world and the IT world. So exactly what is missing today is to have a perfect match between operational data from the machines that are moving things and IT world, which is talking about bit byte JSONs and REST APIs and this kind of kind of languages, okay? The system is also easy expandable. So, we can expand the connectivity with the Type-C port. We have a Type-C port that works easily like a normal port from your computer. We can stick inside the Wi-Fi sticks or memory sticks. We can also use standard microSDs to be used with our microSD slot.
MAURO RIBONI: 10:35
We have three internet ports, one which is preferred to be used with HMIs and engineering, so standard IT port, one which can be dedicated to EtherCAT or other protocols, and an extra port that can be used. So also, from interfaces and the expandability point of view, we have a lot of opportunities, but we will see about that later. So yeah, unfortunately, we are stopped. We are finished with the theoretical part. And now we are going to show really what does it mean to configure InfluxDB over our device and to start to catch some data. So, we’re going to go in the live. So, system configuration is the first step we would like to do. Okay. So, this is my device. So, I’m logged in inside my device. And we can see that on the left, we have the list of the application that we have installed. New one. And we have the settings button. And like any computer, our ctrlX can be set. And we can decide to create new users. We can decide to create a backup restore. Of course, many of our applications have a license, so we have the list of applications and licenses that we have installed on the device. And we have apps. So, we can go in the apps, and we can decide to install different apps.
MAURO RIBONI: 12:28
Now, since my device is connected to internet, I can see a lot of applications available, but not all of them is what we need. And I decided to install directly from a file locally, which is easier for me. We start configuring InfluxDB, but then we need in the future also to show the data loading node thread. One use case is created, implementing Telegraf. Wait a second, did it wrong. Telegraf, then Node-RED, then in InfluxDB. Okay. And then maybe we would like also to show the data. So IoT Dashboard is also another application we would like to have. So, I’m installing the applications. The applications are being loaded. This can happen also from internet. So, if you are connected to internet, then you can just press on install button and the device is installed in his own application alone.
MAURO RIBONI: 13:37
Okay. Install. And we will see that once one application is installed, we can see the icon appearing here. So InfluxDB is installed. Meanwhile, we are installing the applications, I spent two words about the technology we are using to create the apps. The apps are contained. So basically, we adopted the technology from Ubuntu, which is called the Snapcraft. The application is called Snaps and is sandboxed. So, if one application is dying or containerized, then when one application is dying, and the other ones should not be affected by the faulty applications.
MAURO RIBONI: 15:06
And plus, since all our system is based over Ubuntu, we are talking and using Canonical’s Ubuntu Core modified. Then we are also getting all the security patches and anything around security from Canonical itself. So, the system is always up to date, and we are also ready to react rapidly in case there are some problems with libraries, or we know that there are some problems with the one version as soon as Canonical has released a fix, we can adopt the fixer rapidly with no time. Okay. So InfluxDB is installed. We are installing IoT Dashboard. Node-RED and Telegraf are already installing, but they are not important now, so we can go through the configuration of InfluxDB itself. I go back in Settings. I go in Storage. And yeah, I have already an SSD connected to the USB port, and I can—yeah, I decided to format as FAT EXT4. It’s not suggested to use FAT32 as a file system since it’s not a good file system. So, it’s good to exchange data. But if we would like to use memory to store InfluxDB data, then it’s really suggested to use an external memory.
MAURO RIBONI: 16:51
Why are we using an external memory and not the internal memory? Two reasons. First reason, internal memory is really small. It’s three, four gigabits because we just have to store the applications and nothing more. Plus, this is a really small device. So, it’s our entry-level device and has not many resources. It’s an ARM device with four core and two gigabytes of RAM, so entry-level. But it’s cost-effective that the customer is looking into this. And using an external memory provides us more memory and the possibility not to destroy the internal memory. So, if we use InfluxDB in internal memory, once the internal memory has been used, then the system is, yeah, done. If we use the external memory, we can just replace the memory in case in some 10 years the memory is gone. Because we must remember that all we are doing in an industrial environment should last at least 10, 12, 30 years. That’s the challenge.
MAURO RIBONI: 18:00
So, we can mount SDA highs, SDA 1 for that exchange. Okay. Mount it. Now at this point, we have just to mark down which is our mount point and configure InfluxDB accordingly. To configure the InfluxDB, we have to press over Manage App Data, then go inside InfluxDB configuration, click on Config. And here we have the configuration file of InfluxDB. So yeah, really easily, we are already suggesting the SDA location, so we use this. And also here, yeah, we are suggesting for the other part, the location. And yeah, we use it. I save the configuration. And once the configuration is saved, we can go to InfluxDB. Maybe it needs a restart to accept the configuration. Okay. So, I’ve clicked here in InfluxDB. I’m opening InfluxDB, and I can go through the first configuration. So, Bosch Rexroth.
MAURO RIBONI: 19:53
I configured my username as Bosch Rexroth, password Bosch Rexroth and everything Bosch Rexroth since is not important to me now. Wait a second that I have some small screen problems. Okay. Configure. I copy to clickboard my token and then quick start. And we are ready to go. So basically, we have installed InfluxDB inside our device on edge. This is the open source version of InfluxDB, which is packed by us. And we provide to our customers for a small one-time fee, small, one-time fee per device. There are no recurrent fees. But what we are seeing today is true and good in both situations where InfluxDB installed over the device or when InfluxDB is installed somewhere else. So, I usually suggest the customer to go to InfluxDB configuration, press Plus, and save the token here. The data here, the configurations, who can access this configuration can be configured. In the settings, we have our user permission management, which is basically the Linux users and permissions. And we can decide who can assess the configurations.
MAURO RIBONI: 21:50
So, if we can modify the configurations, so in this case, I have full access. But if we would like, let’s say, not to share that information, what is written in the directory is encrypted, and you need the right user to access the configurations. Okay. Now we have seen that Telegraf has been installed also. Node-RED is installed. Okay. And I would say that basic configuration has been done, and we can go to the second part. So, the second part is—sorry, I clicked wrongly. So, the second technical part is the monitoring general proposal. So, our goal will be to take data from some devices. So, I would say some acceleration from sensor motors. I have a remotely accessed to a demo which is running. And yeah, take them from the data layer and send using Noderead to InfluxDB, and then visualize them with IoT Dashboard, which is Grafana, basically. So, this is our workflow, and we are trying to reproduce it first with random data, and then we see the outcome in a real use case.
MAURO RIBONI: 23:40
So, first, we need internet connection. I got some problem before, and I hope that this is working now. So, because I need that to connect to send the data, okay? So, I go to Node-RED. And I want to download first with the palette InfluxDB. So, I usually use this node, but this can be done also using REST APIs. In this way, I’m installing in Node-RED, a node that I can use to send data in InfluxDB. Yeah, I forgot one step before. InfluxDB is series database. So, it’s important having a right time and date set. So, if you have an NTP server on your network, yeah, you can use that. You can synchronize with your clock. Our device has a battery. So usually, the time is synced, can be that there is a little bit of drifting. So, time is really important. So please use an NTP server or check regularly that the time is set. Okay. So, I load up a demo that I have prepared, and then we go through the demo.
MAURO RIBONI: 25:30
Okay. So, I go through the demo. So, this is an inject. So, I’m injecting some data every millisecond. Then I generate three random values. I add a topic, which is, in this case, a torque force percentage, okay. Speed command and actual speed. I join the three values, and then I send them in InfluxDB. InfluxDB location is localized because— yeah, it’s localized. And the organization is Bosch Rexroth. Bucket is Bosch Rexroth and measurement. I call it drive one. So, I modify this because I have to update the token. So I go to home, and I go get my token back. We can also generate later a new one, but locally, it doesn’t really matter, actually, since everything is closed inside the device itself. And I can deploy. Okay. So, what is happening is that after five seconds, I start every second to load random data inside InfluxDB. So now it should be doing already the trick. So, I can go in InfluxDB and go to see what is happening. And I see that, yeah, I’m basically loading random data in InfluxDB. I’m using random data because, yeah, I have nothing more to use. Okay. But usually, people would like to sample real data. So, I have an example remotely accessible from a portal.
MAURO RIBONI: 27:29
So, this is my device working in our headquarters in Germany. This is a quite complicated setup, but it’s not important for today. What is important is the monitoring part. So, I’m accessing one device, which is really similar to the same I have on my desk, but has some hardware connected. So, I see that I have many other applications, and we have our Node-RED program, which is really similar to the other one, but it’s getting real data. So InfluxDB, okay. So, I’m injecting a timestamp. I’m getting some value. I add some topics. So same structure as before. And then I send in them in MPB10. So MPB10 is the name I’ve given to my sensor. And if I go to InfluxDB, in this case, I will have the information from the sensor instead of random data.
MAURO RIBONI: 29:08
So, the three vibrations sampled. This is a vibrational sensor, and these are the components of the vibration in three dimensions. And this is all. The data are sampled from the data layer, the one I stated before. So, if we go in settings, then that layer, we can see all the data present in the data layer. So, in this case, we are getting data from PLC, application, symbolic, and MPB10. And here are our data. We can get the data so easily because our programmers, our developers, they decided to create one special node, which is, yeah, a no-brain, really, just to get it outside. So, we just have to configure this node. And if we need to sample data not so fast, then it’s really easy. And once the data are in InfluxDB, we can also visualize them with the IoT Dashboard, which is our Grafana version. And yeah, basically, the data are here. These are the vibrational data, and these are the data demo I have done as a demo. And these are some random generated information. Okay. So, this is all for the normal monitoring.
MAURO RIBONI: 31:04
Now we go to the second part, which is real-time monitoring. So, the difference in real-time monitoring is present. Wait a second. With a small screen, it’s always a little difficult. The difference is that with real-time monitoring, we need to sample the data real time. So instead of using Node-RED, we are using Telegraf. Inside Telegraf, there is a plugin done, especially by us for the data layer. And this plugin is able to get this data real time, make a buffer, and load it up the buffer automatically with the right timestamp inside InfluxDB, and then can be visualized. And all of that without programming. So, this is the scheme. We can give a look again. And especially with the difference before, you see that here there is a lossless communication, which is an asynchronous communication, which can create this buffer. So, the data layer has an extension which can automatically do that. Before we had no real-time communication, this is the principal difference. But to do that, we have to understand which kind of data we can sample. So again, in our data layer, we go in real-time data, real-time, and data, again, and xVibr. So, we see that this value is changing. And we see that in the extensions, we have qos.lossless: true.
MAURO RIBONI: 32:57
This value, this extension tells us, yeah, you can use this configuration inside your device. And you can sample real-time your data. This is present also in Fieldbus. So, if we would like to do the same thing with Modbus TCP or EtherCAT, for instance, we can go in EtherCAT. So, output—or input probably because it’s an input. This is the name of the bus coupler, and these are the information exchanged. So also, here we can see the extension through. So, if we have an I/O, if we have an analog input, or also we would like to monitor an analog output, this kind of data can be sampled. So Telegraf, we go in Telegraf. This is our version of Telegraf. I have to move myself another time. Okay. And yeah, we have two configurations. One is a dummy configuration. This is my actual configuration. To add a configuration, you just have to add plus, okay? And when we click on Plus, we have a list of demos. So, a demo for monitoring. So, I just looked at this. Okay. And we can then edit that.
MAURO RIBONI: 34:39
So, we have first token organization bucket and all the information, credentials, and then we can—in our control input—we have the possibility to configure the measurement and to give a list of nodes from the data layer where we can sample data. So now we are going to finish this. I’m copying from the actual configuration. Okay. Save. Then I copy the other part. Username and password. Yes, it’s always Bosch Rexroth in our demos to remove the complexity of. Okay, and then should be fine. So, we can start the configuration and then check the logs. So, we can go through the code. So basically, we are saying sample these nodes and send them with an interval of 10 seconds, basically. This Telegraf can be used to sample also Node-RED time data, and this is what we are doing now. So, in this situation, if we’re going to InfluxDB and we check Metrics, we can have our CPU usage sampled. This is our CPU usage sampled directly from the ctrlX CORE. But we would like to sample real-time data.
MAURO RIBONI: 36:38
So, without going through all the difficult things and how it has been programmed and so on, in the configuration, we have some parameters to be tuned. So, I’m just showing that for the real-time part, for the non-real time part, it’s similar, but I mean, it’s not important now. So, I would like to sample three values. So, one is real-time data from that vibration and then temperature, okay, with a sample interval zero. This is just a trigger to say, I want to get all the data. So, sample interval is zero. Loss rate limit is one, is that they multiply. So, one means I get all the data. Two means I get one data and two, three means I skip two data, and then I get the third, and blah, blah. Two size is two sizes. So, I created a buffer of 100 elements, and then I am sending them to InfluxDB. These are two tuning parameters because, of course, if I use that with four devices for data, then it’s not so complex. It also depends on how fast my field pass is. So, if I’m sampling at one millisecond, then it can be that I would like not to get all the data, but just one over three and something like this.
MAURO RIBONI: 38:08
So, with this configuration, what is happening is, yeah, basically the same, but in real time. So, I can go to Metrics real time and I’m really getting all the values. So, I’m really getting all the vibration and all the information inside. And yeah, this is the configuration. I know that can seem easy, can seem not so complex, but it’s automatically. So, we are sampling the data and storing somewhere without programming. So, we can also come here to say, okay, I don’t want to save it here, but I want to save it real time for monitoring purposes in my data center, in my server, or I can also decide to store someone else. Usually, the customer, they would like to have two configurations. One is normal, one that is indicated somewhere else. And in this way, we can do it without programming. And yeah, we are kind of free to do what we prefer.
MAURO RIBONI: 39:24
Okay. So back to the presentation. We are in the final phase. So why the ctrlX CORE and why— I mean, I know that this is possible also to be done over different kind of devices and why ctrlX OS. I know that for IT people, it’s difficult to understand why you have to create a new device for industrial environment. First of all, the devices should be industrial. So, if something happens to my system in 10 years, I have the replacement part. Old backup/restore mechanism is achieved in our ctrlX OS. Second, we are offering or also our partner, they are offering different kind of hardware. So, if our device is not correct and we would like to use a device which is specially designed to an environment, I think about Railways, I think about devices that should be IP68. We give the possibility to start ctrlX OS over these special hardwires without changing [inaudible]. And second, then we also give a scalability. So, we have the standard one with the one I have on my desk, then a medium-sized one, which has also some external port to communicate with ETERNITY PE, [inaudible], and other protocols. Then we have bigger guys with other kind of ports and many other ports and more RAM and more memory inside. And the end of— the biggest one is the ctrlX CORE X7, which has an i7 and 32 gigabyte of memory and 16 gigabytes of RAM.
MAURO RIBONI: 41:23
We will have versions with the AI accelerator TPUs inside and other kind of extensions. So, what we are doing is achieved without programming and in an architecture which is standard and Cyber Resilient Act ready. So, in Europe, we have this regulation that all the devices we are selling should be patched and they should be resilient over cyber attacks. And our system is ready to outstand this kind of regular [inaudible]. Okay. And this is all from my side. And this is now the Q&A section. So, I leave you two QR codes so that you can get to our ctrlX OS website, meanwhile, and all the code and all the configurations and the description of the use case I’ve shown today can be found here in our community. So, I’m here to answer your questions.
BAILEY MELLOS: 42:41
We do have a few questions. A couple of them were in the QA. So, one of them is what is the operating system of the edge devices? They wanted to know if it was Debian?
MAURO RIBONI: 42:53
Is Ubuntu-based operating system real-time. So, the operating system is really real-time. So, we can implement 1 millisecond, 0.5 millisecond, 2 milliseconds task inside. We have a scheduler. It’s based on Ubuntu with some real-time part extension we have done ourselves. So, this is the operating system.
BAILEY MELLOS: 43:19
Great. Thank you. Same attendee wanted to know, do the applications that they develop internally need to be installed as Docker containers?
MAURO RIBONI: 43:28
We are going also to the direction to use Docker containers and not in the small guys, but in the biggest controllers. Usually, our native version of containers are Snaps from Snapcraft. But with the container and giant, there is also the possibility to run Docker containers. We prefer Snaps because Snaps are more secure. With Docker containers, we cannot certify any more security, cybersecurity, but can be done.
BAILEY MELLOS: 44:10
And then another person would like to know what happens to the process data if, for example, the external memory is full or becomes full with logging time, and the retention policy of InfluxDB has not yet been reached.
MAURO RIBONI: 44:24
Yeah. At that point, you fill up the memory and probably the InfluxDB goes to error. We have the possibility to, let’s say we have the possibility inside the data layer to set some thresholds so we can set some alerts for these kinds of things, but it’s up to the user to do that. And there is no automatically fallback mechanism or whatever at the moment.
BAILEY MELLOS: 44:53
Thank you. And then the last question so far is, is there a rule of thumb when to use Node-RED or Telegraf? Is there a big benefit over one or the other?
MAURO RIBONI: 45:04
Usually, people have different kind of necessities. So, Node-RED can be expanded with the palette, and we can install a lot of different libraries to be used to communicate with other devices. And plus is a standard. So, people that are using Node-RED and they’re comfortable with Node-RED, they usually use Node-RED. This is mostly more a cultural and a standard thing because Node-RED is kind of a standard. Telegraf is super when the data are already in the data layer, and we are getting that from the data layer automatically or we would like to get data real time. So, this is the real turning point. So, if you’re comfortable with Node-RED and you have simple data, just use Node-RED. If you want real-time data or have all of them in InfluxDB in the data layer, you want to send them to InfluxDB, then use Telegraf. That’s the difference. Yeah.
BAILEY MELLOS: 46:10
Thank you. Cool. Richard, I see that you have your hand raised. Oh, we do have another one. If you wouldn’t mind dropping your question in the chat, Richard, we would love to answer it for you. And then meanwhile, we do have another question that came in, which is what kind of hardware do you usually use for external storage?
MAURO RIBONI: 46:29
One of the devices we are going to get in the next version will have a built-in SSD. So, we will get one device with an SSD inside. At the moment, I suggest using a microSD card with long-term capabilities. So, the microSD cards used in record cameras or USB SSDs. This is what I suggest using. But we are going to get the appropriate order in 2025 Q1. Yeah.
BAILEY MELLOS: 47:09
Okay. And then do you have a Wi-Fi-enabled model?
MAURO RIBONI: 47:14
A Wi-Fi-enabled model? No. We are allowed to use consumer USB sticks. Not all of them. We have a list of chips that are supported. But we have not yet a device with the Wi-Fi built in. We are working on it, but we are not yet there. Yeah.
BAILEY MELLOS: 47:40
Great. Well, I think that’s all of our questions for today. So just wanted to thank everyone for attending. Thank you, Mauro, for hosting and walking us through all of that. We’ll be sending out an email tomorrow with the slides and the recording of everything. Sorry. I didn’t mean to interrupt, Mauro.
MAURO RIBONI: 47:56
No, thank you. And thanks to InfluxData to have asked us and have given us the possibility to explain our integration.
BAILEY MELLOS: 48:05
Yeah. Thank you so much. We appreciate you being here. And yeah, for everyone attending, feel free to reply if you have any questions, or you can always find us in our community Slack channels. Send us an email. And otherwise, I hope you have a great rest of your day or a great evening.
MAURO RIBONI: 48:21
Thanks.
BAILEY MELLOS: 48:23
Thank you.
MAURO RIBONI: 48:23
Thanks very much. Bye-bye.
BAILEY MELLOS: 48:25
Bye.
[/et_pb_toggle]
