Low-Code in 30 Webinar: Leveraging IoT to Improve Decision Making

• Low-Code Development

• Transcript

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  Hello. Welcome to low code in 30.

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  My name is Simon Black, and I’m a platform evangelist

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  here at Mendix.

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  In today’s low code in 30 we will be

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  leveraging Iot to improve decision

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  making in

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  today’s weapon are well. First, take a look at the

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  local and thirties that you might have missed previously.

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  We’ll take a look out Min Dex as

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  a loco platform.

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  Well, then address. What is I ot

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  at the use cases that can be

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  built using I ity platforms

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  on the difference between those different

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  types of coyote with their industrial

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  I ot over their traditional

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  I ot

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  and finally would showcase endeavor off

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  the medics platform.

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  Leveraging AWS I ot at

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  the Seamen’s Monty a platform.

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  If this is your first time joining us for

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  located 30 then welcome.

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  But these local and thirties have been running over

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  the last few months. We’ve covered a

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  range of topics.

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  All of these could be found on the mend nets dot

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  com slash Denver section

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  or on our YouTube channel,

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  so make sure you subscribe so you can get

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  all the latest updates and information

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  about the medics platform.

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  For those of you that are new to mend

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  ex. I just want to give you a high level overview

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  of what the medics platform is.

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  The medics platform is really trying to

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  incorporate

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  all types of makers within your organization.

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  And when we talk about maker, Maker could

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  be someone that’s from the business building a spreadsheet

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  through to a traditional developer

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  who’s building applications using code.

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  We allow them to collaborate a developed

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  together using our platform.

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  And we have different ideas to support

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  those different types. Developers

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  we have to mend a studio focused out

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  our traditional business developers,

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  and we have the medic studio pro

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  focused at our developers

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  in organization, who might want to write

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  code in extensions and integration

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  possibilities

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  on. By combining both of these two types

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  of developers on being up to collaborate

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  with both the business and I t.

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  We’re able to build applications 6 to 10

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  times faster

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  than you would do traditionally using

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  traditional code.

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  And if you want more information on Vendex,

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  then you can check out one of our previous webinars

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  will be going to more detail about how

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  the platform addresses this development

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  process.

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  If we take a look at i o t

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  ot has been on a tremendous

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  growth over the last few years,

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  and it’s a topic that’s very close to my heart.

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  I’ve covered a number of weaponized focusing

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  on my index and I ity

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  and back in 2017 when I first did my

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  first Iet weapon are

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  I o. T. Was more of a buzzword.

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  People were thinking about using

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  I ity,

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  and we’re starting to experiment with what

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  the possibilities could be when adopting

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  gaiety.

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  Over the last few years, we’ve seen this

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  experimentation

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  coming to realization,

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  and people are building their

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  core capabilities around

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  Internet of things,

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  of building it so that they can compete against

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  competitors

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  and allowing them to make smarter decisions

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  within their organization.

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  And this is shown by the growth

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  in I ity. In terms of the numbers

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  we’ve seen this year, 26,000,000,000 devices

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  being connected up to the I T

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  and this is expected to grow to 75,000,000,000

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  connected devices by 2025

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  on everything from your health

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  care provider through to your car

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  will be connected into the Internet.

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  Things

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  is therefore important. It’s that

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  you adopt this critical capability

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  and build new experiences

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  for your customers utilizing this

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  data,

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  and I just want to highlight four customer use

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  cases here.

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  These are all

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  applications built using the Mendez platform

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  that leverage I ot to enable

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  them to compete on label them to bring

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  new offerings to the market.

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  The 1st 1 is Horta lox

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  on whole talks is a lightbulb company

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  that provide light bulbs to greenhouses,

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  and what they saw in the market was that they were being out

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  competed and outsold

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  by cheaper try these brands.

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  So in order to enable them to differentiate

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  from their competitors,

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  they built this platform on Bendix

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  that leverages aws I ot

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  to track the performance off their

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  light bombs.

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  And if a light bulb is going down

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  in performance,

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  they can get early warnings and notifications

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  on allow them to then change those lightbulbs

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  within the greenhouse,

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  being able to give added value

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  to their customers rather than just

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  providing the lightbulbs itself.

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  We then have lift insight who

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  utilize io ti to track

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  the performance off their lifts on

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  enable them to predict when

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  they need to, actually before maintenance,

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  so they use the data from the ire to platform

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  along with machine learning

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  to allow them to create early warnings

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  and predictions for certain maintenance

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  so they can send their engineers to those lifts

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  and fix them before the issue occurs.

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  We then have Otto

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  and also has

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  created their organization around

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  being able to perform analytics

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  on how above building is currently

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  performing or an office,

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  and now enabling their customers to get

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  insights into how much occupancy

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  they have.

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  How much temperature is being used,

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  or energy being used within each of the rooms

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  enable them to get insights and information.

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  So they provide that full stack everything from

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  the sensors up to experience

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  Layer bill in Bendix so that their

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  customers can get

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  really good insights into how

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  a building or office is currently

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  performing,

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  enabling them to build a smarter

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  decisions

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  into their organizations.

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  And finally, we have an tell on Antero

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  primarily work with pharmaceutical customers

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  on pharmaceutical. Customers often want to be

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  able to track and trace

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  certain Transportacion off their products,

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  whether that be a drug or device.

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  Often these need to be transported

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  to the consumer in certain conditions.

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  They might be need to to transport a certain temperature.

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  They might have certain lights, conditions

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  also movement conditions that need

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  to be adhered to to make sure that

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  that drug is as effective

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  as possible.

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  So bye, you sighting. I ot information

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  that sensors on those particular device

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  is able to see this information

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  on alert. Certain users in

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  this mend its dashboard

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  so that they can go and check out that particular

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  item when it’s not been transported good,

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  and also take it off the shelves if

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  required, if it’s not been stored at

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  the right temperature.

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  So these are just

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  four use cases

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  off Vendex customers utilizing I

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  ity, but there’s a

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  huge amount off opportunity

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  for many more customers to leverage.

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  I ity on the data that you get

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  from being over to create

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  those experiences on top of a nightie

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  platform.

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  But in order to create these experiences, you

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  number. You need a number off layers.

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  First of all, you need a cloud infrastructure that’s able

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  to scale.

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  You need to them build a platform is a service

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  on top of that, so something market cloud foundry

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  or Cuban ITI said you can scale the

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  number of instances in the number off

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  environments of running on that count infrastructure,

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  you need to suffer is a service solution that provides

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  you with these capabilities to be able to

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  scale and add those applications,

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  and they suffer solutions. Often integrate

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  to APP service is whether that be I ot

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  service is or whether that be Machine Learning Service

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  is on top of

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  the platform, the service’s

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  And if we look from the bottom up, we

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  often need certain things. We need to

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  monitor devices, whether it be a phone,

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  wherever it be a computer or whether it

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  be a

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  fridge freezer, for instance.

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  And those things often communicate with many

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  different protocols, and that data needs

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  to be ingested and stored somewhere.

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  This is where an ingestion layer will take.

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  Those particular things allow

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  you to communicate with them

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  proficiently

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  and be able to then

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  have those within your cloud infrastructure.

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  But to utilize all these layers, it’s very

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  difficult in a traditional platform and

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  a traditional coding languages,

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  and that’s where loco comes in. Loco makes

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  it much easier, futile,

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  integrate into each of these layers

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  on be able to provide those

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  experiences

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  that really make it beneficial

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  to use on io ti plateful.

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  And when we took what i o. T. often people

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  are very confused about what I. O. T M.

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  What coyote means

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  on dhe over the last few years has been an

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  emergence in two types of piety.

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  We see on the left hand side industrial I

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  ot, which is really focused that

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  smart cities transportacion heavy

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  machinery

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  as well as trying to improve your automation

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  within your manufacturing, processing factories

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  and health care.

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  And on the Ryan side, we have consumer I ot,

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  which is looking at home automation,

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  being able to look at wearable devices

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  of phones and TVs and how that data

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  can be utilized within a platform

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  on all of these need to go into a cloud

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  solution so you can monitor those.

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  You can make smart decisions upon

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  days

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  whether that be AWS minds fear

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  as your

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  or whether you want to use broker

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  like mosquito for your

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  communication between those devices.

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  So last year, Minnix was acquired

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  by Seaman’s

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  on Dhe. One of the reasons we were acquired

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  was because we could rapidly create applications

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  that utilize I ot data

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  on one of the platforms that Siemens offers

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  is months fair.

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  Um, I see is the crown based open

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  I ot operating system,

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  allowing you to connect into any data,

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  whether that be from a sensor, a device

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  or any other system.

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  It allows you to consume that and store that

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  within the mindset platform.

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  But you need to be able to create experiences

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  and applications on top off the mines here.

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  Platform.

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  So this is why men, Dicks and

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  Seaman’s

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  work together to be able to build a

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  integration, first of all,

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  so that you can deploy your applications and

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  leverage to single silent service is the

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  mines the AP eyes and create

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  digital twin experiences on top of

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  those so really focused

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  out that industrial I ot space

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  began to monitor actual physical

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  products out of the market and

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  be able to then get insights and change

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  the product in your PLM

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  software.

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  So low code for I ot makes it much

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  easier for you to be out between Dragon Drop

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  and build those experiences. Leveraging

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  that data from the mindset platform

  [00:11:52.046]
  and it allows you to collaborate

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  much more with your I T.

  [00:11:56.625]
  O T. Communities,

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  allowing you to unleash the talent

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  is available within your OT talent

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  pool to build applications

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  6 to 10 times faster than traditional

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  developed. So

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  let’s take a look how we can leverage

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  the manage platform

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  to utilize i ot

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  festival will take a look at how we can use

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  a DBS I ot

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  and then we take a look at how we can deploy

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  and utilized data from the seaman’s

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  minds here. Platform

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  in this demonstration will take a look at

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  how we can extend an existing application

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  built on the mend. Its platform.

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  This particular avocation was built in

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  our previous Loco 10 30.

  [00:12:42.328]
  In those low code and thirties. My colleague Jeffrey

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  Goldberg showcased how we could improve

  [00:12:46.958]
  this emergency service’s application

  [00:12:49.369]
  by leveraging conversational platforms

  [00:12:52.019]
  to enhance our U X

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  and also deliver a I in cognitive

  [00:12:56.759]
  service’s with ease

  [00:12:59.448]
  in those two weapon ours, Jeff showed

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  how we could leverage

  [00:13:03.609]
  messaging bots like slack on

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  what’s app to be able to engage you

  [00:13:07.849]
  of our customers

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  and also

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  in the A. I in cognitive service

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  is one he looked at how we could utilize

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  image recognition to detect certain

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  incidents.

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  So whether that incident be a cat

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  up a tree

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  or whether it be a car accident,

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  we want to take this one step further. We

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  want to be able to extend this application

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  and leverage i o T data so

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  we can make smarter decisions.

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  This particular application has been hooked up to

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  a number of graphs here

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  so that we can see temperature come

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  dark side and carbon monoxide from

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  certain buildings.

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  Currently, this application isn’t hooked up

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  to any I ot service,

  [00:13:51.408]
  so we need to able we need to be able to

  [00:13:53.989]
  hook into that data

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  and make certain decisions around it.

  [00:13:59.089]
  We have already an application

  [00:14:01.208]
  that is sending data to an eye OT

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  service,

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  and in this case, we’re using the AWS

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  i ot service.

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  End of society is a very popular platform

  [00:14:14.025]
  for really experimenting and building

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  applications very quickly on

  [00:14:18.316]
  top of Iron T platforms

  [00:14:21.466]
  allows you to monitor connections

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  on DDE

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  inbound and outbound activity.

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  It allows you to onboard devices

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  and functionality very easily,

  [00:14:33.905]
  as well as manage things

  [00:14:36.166]
  which are essentially representations off

  [00:14:38.395]
  certain devices.

  [00:14:40.365]
  You might have multiple devices

  [00:14:42.885]
  that belong to a particular thing,

  [00:14:45.316]
  and a thing always has a certain type

  [00:14:48.096]
  which essentially describes what

  [00:14:50.096]
  the thing does or doesn’t do.

  [00:14:53.975]
  Currently, we haven’t application

  [00:14:55.875]
  that is submitting this data every

  [00:14:58.125]
  few seconds

  [00:14:59.546]
  and its publishing that data to the

  [00:15:01.625]
  A. D. B s I o t service.

  [00:15:04.456]
  And if we want a view that data, we can simply subscribe

  [00:15:06.966]
  to that information using this test

  [00:15:09.145]
  harness here within

  [00:15:10.556]
  the AWS IittIe service.

  [00:15:14.035]
  In this case, we’re using a diverse M

  [00:15:16.086]
  ke t t

  [00:15:17.515]
  and M ke. T t is a very lightweight protocol

  [00:15:20.135]
  for testing and integrating

  [00:15:22.365]
  into data

  [00:15:24.645]
  in neighbour’s you to very quickly send

  [00:15:26.946]
  data from coyote devices but

  [00:15:29.135]
  also receive it as well.

  [00:15:31.995]
  By subscribing to a topic, we

  [00:15:34.066]
  can see all of the information that is published

  [00:15:36.875]
  to this topic

  [00:15:38.216]
  and the other two operations. When working

  [00:15:40.265]
  with m ke t t

  [00:15:41.956]
  you subscribe a k listen

  [00:15:44.416]
  to data

  [00:15:45.495]
  on publish a K pushed

  [00:15:47.546]
  data to the crowd.

  [00:15:50.485]
  And as you can see here, we’re getting live streams of

  [00:15:52.546]
  data now from our I T device.

  [00:15:55.586]
  And if we want to use this data within the

  [00:15:57.625]
  medics platform,

  [00:15:58.796]
  we need to be able to map this in and

  [00:16:00.806]
  be able to use it within our entities

  [00:16:04.515]
  to do so. We can actually export this definition

  [00:16:07.125]
  here so that we can start to use

  [00:16:09.235]
  this within our application.

  [00:16:11.113]
  A map it into data within

  [00:16:13.363]
  our app.

  [00:16:15.003]
  So if we copy this payload, we can then

  [00:16:17.163]
  use this within our development

  [00:16:19.383]
  environment.

  [00:16:21.503]
  So if I switch over into men Dick Studio

  [00:16:23.783]
  Pro

  [00:16:24.643]
  this is where we can build out the experience

  [00:16:27.062]
  for those applications.

  [00:16:29.722]
  We can build out the pages. So what the user’s

  [00:16:31.952]
  sees within the application,

  [00:16:34.952]
  whether it be a, uh

  [00:16:36.972]
  oh, view, dashboard grid

  [00:16:39.202]
  and so on,

  [00:16:40.222]
  we can drag and drop these components onto

  [00:16:42.243]
  here. We can also

  [00:16:44.253]
  build out logic within the platform.

  [00:16:47.312]
  And in order to get the data from

  [00:16:49.743]
  the A. D. B s i o T service, we

  [00:16:51.793]
  need to subscribe to that data using

  [00:16:54.363]
  our and PTT connector.

  [00:16:57.413]
  So you can see here we have our connector

  [00:16:59.773]
  with a number off property set.

  [00:17:02.602]
  And this is currently utilizing the

  [00:17:04.722]
  DBS connector that’s available

  [00:17:06.992]
  on our APP store.

  [00:17:09.232]
  And our lab stories are component Library

  [00:17:11.673]
  allows you to extend both on the back ends

  [00:17:13.932]
  with connectors

  [00:17:15.423]
  on dhe

  [00:17:16.292]
  AP eyes

  [00:17:17.522]
  as well as widgets and themes

  [00:17:19.792]
  for your front end extensions,

  [00:17:23.053]
  and you can download thes and reuse these across

  [00:17:25.282]
  your application

  [00:17:26.633]
  to allow you to accelerate that development process

  [00:17:31.752]
  within the M t t t

  [00:17:33.813]
  subscribe options we have the

  [00:17:35.893]
  option to power is the message

  [00:17:38.093]
  that we get back from the M cruelty

  [00:17:40.212]
  to service.

  [00:17:42.063]
  If you click show, we can go into that flow

  [00:17:44.532]
  and see we have two parameters.

  [00:17:47.012]
  A topic on a payload.

  [00:17:50.962]
  To use the payload data, we need to map that

  [00:17:53.123]
  into our application.

  [00:17:54.482]
  That’s very easy to do using Jason

  [00:17:56.792]
  Structures and Jason happens,

  [00:18:04.093]
  we can pace the data here,

  [00:18:06.053]
  format it on refresh, and

  [00:18:08.202]
  it will automatically pick up the data items.

  [00:18:12.884]
  Next thing we need to do is need to map that data

  [00:18:15.314]
  inter application so we can use it.

  [00:18:18.084]
  We create an input mapping.

  [00:18:19.844]
  We could select the structure with just create it.

  [00:18:23.433]
  We can check elements that we do or don’t

  [00:18:25.523]
  want, and then map that

  [00:18:27.923]
  against data from are the main

  [00:18:30.094]
  model. If

  [00:18:35.233]
  the names are matching automatically,

  [00:18:37.433]
  then they will match their of

  [00:18:39.594]
  wise. It will create new ones for us.

  [00:18:43.233]
  Go back to the process flow. We can now import

  [00:18:45.693]
  that data and start to use it within our

  [00:18:47.824]
  application.

  [00:18:51.263]
  We select import mapping,

  [00:18:53.544]
  we can select the data we want to import

  [00:18:56.364]
  and the mapping we want to use, which is the one

  [00:18:58.403]
  we just created.

  [00:19:00.423]
  We can choose to save it

  [00:19:01.884]
  and also story in a variable.

  [00:19:08.253]
  The next thing we need to do is update our dashboard.

  [00:19:11.094]
  We want to be over to see the information

  [00:19:13.804]
  that we get back from our

  [00:19:15.753]
  a p I

  [00:19:20.794]
  Once we’ve got the fire alarm details

  [00:19:23.094]
  from the I T service, we want to make some decisions

  [00:19:25.473]
  around it. Amusing exclusive

  [00:19:27.614]
  split here. We could make some decisions

  [00:19:29.773]
  around our temperature data.

  [00:19:33.384]
  So if the temperature is going to go above

  [00:19:35.554]
  60 degrees

  [00:19:37.094]
  that we can assume that this

  [00:19:39.193]
  is potentially an opportunity for

  [00:19:41.263]
  a fire.

  [00:19:42.653]
  So 60 degrees associates is quite

  [00:19:44.864]
  high, so we could ultimately predict

  [00:19:47.503]
  that there is a potential for a fire.

  [00:19:59.064]
  If there is, it will go down this true line.

  [00:20:01.824]
  A voice. It would go down the false line which

  [00:20:03.973]
  will draw in a second.

  [00:20:12.374]
  We’re gonna create a new emergency event

  [00:20:14.693]
  based on the data we get back from

  [00:20:16.763]
  the service. We’re going to say

  [00:20:18.804]
  really predict

  [00:20:21.604]
  this building

  [00:20:23.253]
  is going.

  [00:20:24.753]
  People catch

  [00:20:26.423]
  the fire.

  [00:20:31.203]
  We can say what type of emergency is

  [00:20:33.884]
  in this case is going to be a fire

  [00:20:56.943]
  and then we just need to deal with the false

  [00:20:59.074]
  line and the first line. We’re

  [00:21:01.243]
  not gonna do anything. We’re gonna ignore that

  [00:21:03.334]
  information.

  [00:21:07.854]
  So now we’ve connected up this subscribe

  [00:21:09.894]
  button to the overview. We just need to

  [00:21:11.923]
  make sure that this data updates.

  [00:21:14.314]
  So I’m gonna add on a snippet here

  [00:21:16.874]
  to make sure our dashboard always refreshes

  [00:21:19.844]
  and make sure it always has the latest data.

  [00:21:24.304]
  The final thing we need to do is now run this. We

  [00:21:26.433]
  need to run this locally so we can see that information

  [00:21:29.193]
  and make sure we’re getting all of that data

  [00:21:31.493]
  from the i o. T. Service.

  [00:21:34.304]
  So very quickly, we’ve been our to

  [00:21:36.673]
  create a mapping based on

  [00:21:38.693]
  the data we’re getting from R I

  [00:21:40.773]
  o T service in a dress

  [00:21:42.953]
  we’ve been out to then create some logic

  [00:21:45.344]
  and some decisions in a mark float.

  [00:21:47.814]
  And now we’re re running this application so

  [00:21:50.054]
  we can view it in our browser.

  [00:21:52.993]
  So if you go back to the Mendez dashboard

  [00:21:54.993]
  here Onda, we hit Refresh.

  [00:21:58.003]
  We’ll be able to see the information starting to

  [00:22:00.114]
  come into a platform.

  [00:22:02.584]
  We can see the temperature, the humidity

  [00:22:05.344]
  and also the carbon monoxide levels.

  [00:22:10.193]
  But in order to get that data, we need to

  [00:22:12.273]
  subscribe to it.

  [00:22:14.451]
  If we’re not subscribed, we won’t receive

  [00:22:16.711]
  any information into this dashboard.

  [00:22:20.540]
  So now that we’ve subscribed, we should now start

  [00:22:22.901]
  to get events into our application.

  [00:22:25.911]
  And you can see here we’re starting to get new data

  [00:22:27.941]
  for our com dockside

  [00:22:29.540]
  temperature and also our carbon monoxide.

  [00:22:34.641]
  We could see those values increase over

  [00:22:36.691]
  time to increasing

  [00:22:38.851]
  the cover. Not sighing come dark side,

  [00:22:40.931]
  which is never good.

  [00:22:42.851]
  It’s a clear sign of a fire,

  [00:22:45.221]
  but we’ve added the rule in for the temperature.

  [00:22:47.431]
  So if we increase that above 60

  [00:22:50.810]
  let’s go Thio

  [00:22:52.461]
  80 degrees.

  [00:22:54.361]
  We will see then

  [00:22:55.750]
  that will get a new ruler into our application.

  [00:22:59.201]
  Here we are. So we predicted this building’s gonna

  [00:23:01.250]
  catch fire

  [00:23:02.570]
  and we can see the level at which

  [00:23:04.931]
  is predicted is going

  [00:23:06.500]
  to go to a swell

  [00:23:12.961]
  so very quickly in the last 10 minutes

  [00:23:15.290]
  will be enough to show you how we can extend

  [00:23:17.391]
  an existing application for emergency service

  [00:23:19.750]
  is and we can make smarter,

  [00:23:21.780]
  intelligent decisions

  [00:23:23.421]
  using I ot on information

  [00:23:26.111]
  from a T V s I o t

  [00:23:29.351]
  In Our second demonstration will take

  [00:23:31.381]
  a look at this seaman’s mind street platform

  [00:23:34.000]
  And how many dicks applications could be deployed

  [00:23:36.661]
  onto the platform and leverage

  [00:23:38.661]
  data. They distort their.

  [00:23:42.141]
  So we looked into the minds here, platform here,

  [00:23:44.540]
  and we can see here we have a number of tiles.

  [00:23:47.490]
  These tiles allow us to manage assets,

  [00:23:50.211]
  also applications.

  [00:23:52.030]
  And also single sign on into applications

  [00:23:54.270]
  Bill in men dicks.

  [00:23:56.851]
  If you go into the Asset Manager, this is where

  [00:23:59.000]
  we can configure certain data

  [00:24:01.711]
  assets and devices that were actually

  [00:24:03.800]
  going to monitor.

  [00:24:05.750]
  In this case, we want to monitor a pump we

  [00:24:07.750]
  could see here. We’ve got some details and a photo.

  [00:24:10.461]
  We can assign a particular asset to a

  [00:24:12.560]
  type

  [00:24:13.530]
  allowing us to monitor multiple assets

  [00:24:15.701]
  against a particular type.

  [00:24:18.261]
  And then we have aspect ce on aspect. So

  [00:24:20.362]
  what we’re going to measure

  [00:24:22.132]
  these might have multiple attributes and

  [00:24:24.372]
  assigned

  [00:24:25.402]
  to particular assets.

  [00:24:27.442]
  So in this case, we’re monitoring for the pump

  [00:24:29.912]
  room monitoring current

  [00:24:31.811]
  the motor currents pressure in

  [00:24:34.011]
  on also pressure out.

  [00:24:38.041]
  Once we set up the assets, we then need

  [00:24:40.201]
  to be out to configure the applications

  [00:24:42.551]
  that are gonna leverage that data

  [00:24:44.551]
  and to do so, we can use the developer

  [00:24:46.711]
  cop it here to set up new applications

  [00:24:49.531]
  on new credentials, which we can

  [00:24:51.531]
  then use within our Mendes applications.

  [00:24:55.001]
  These credentials allow us to connect

  [00:24:57.021]
  into the mindset data, but also

  [00:24:59.211]
  used a single sign on service.

  [00:25:01.541]
  We can add configurations in

  [00:25:03.582]
  here as well. Two actually customize

  [00:25:06.311]
  our application

  [00:25:07.521]
  such as environment variables or

  [00:25:09.531]
  operations and information

  [00:25:11.602]
  that we need particularly to this environment.

  [00:25:17.981]
  If you go back to the dashboard, we can see that we’ve

  [00:25:20.011]
  already provisioned on example

  [00:25:22.041]
  application.

  [00:25:23.342]
  The Pump Asset example is

  [00:25:25.561]
  an app that’s available on the medics

  [00:25:27.761]
  APP store. You can download this

  [00:25:29.942]
  and configure this to get idea

  [00:25:32.132]
  as to how to develop an application

  [00:25:34.162]
  on top of mind sphere

  [00:25:35.582]
  on leverage the data.

  [00:25:38.051]
  If we could go on goto assets, we

  [00:25:40.061]
  can see here we have a number off data

  [00:25:42.461]
  that is already available. With this application,

  [00:25:46.142]
  we could see current

  [00:25:47.942]
  pressure in on also pressure out

  [00:25:50.451]
  off our particular pump

  [00:25:52.701]
  and we can create this data, use an example

  [00:25:55.192]
  flow, and it shows you how to put

  [00:25:57.271]
  data inter mind sphere, but also

  [00:25:59.301]
  read data from it as well

  [00:26:03.741]
  as I mentioned this particular example is available

  [00:26:06.211]
  on the mend its APP store.

  [00:26:07.832]
  From here, you can search for certain minds

  [00:26:09.852]
  fit components,

  [00:26:11.301]
  which allow you to accelerate your development

  [00:26:14.041]
  things like

  [00:26:15.201]
  Asset Manager connector,

  [00:26:16.877]
  single sign on service

  [00:26:18.948]
  theming packages

  [00:26:20.678]
  as well as full applications like the pub asset

  [00:26:22.877]
  example.

  [00:26:26.157]
  And we’ll be adding more more examples

  [00:26:28.488]
  to this app store over the next

  [00:26:30.498]
  coming months.

  [00:26:34.548]
  So if you need some guidance and

  [00:26:36.567]
  some documentation as to how to set up

  [00:26:38.647]
  and provisioned your own application,

  [00:26:40.567]
  you can go to the docks. Stop mend ex dot com

  [00:26:42.968]
  and search reminds Fear.

  [00:26:44.528]
  And there’s a whole set of guidance, as do

  [00:26:46.728]
  how to set up this particular application

  [00:26:49.538]
  and also how to set up your application

  [00:26:51.948]
  for deployment and configuration

  [00:26:54.387]
  for certain users and also

  [00:26:56.397]
  certain data within your application.

  [00:26:59.548]
  So you’re really helpful Guide, and I definitely

  [00:27:01.788]
  advise you going through this. Just

  [00:27:03.807]
  start building your first application.

  [00:27:07.548]
  So let’s take a look at what that application

  [00:27:09.587]
  entails and what you get from the pump

  [00:27:11.827]
  Asset. Example.

  [00:27:14.157]
  If we switch over to the minute Studio Pro,

  [00:27:16.857]
  we can see here, there comes with

  [00:27:18.887]
  some example. Mike flows

  [00:27:21.087]
  where we can perform logic to authenticate

  [00:27:23.827]
  with the

  [00:27:25.127]
  minds, fear platform using tokens,

  [00:27:28.198]
  and we can then call certain rest AP

  [00:27:30.357]
  eyes that are available via minds

  [00:27:32.617]
  for a P I.

  [00:27:34.107]
  And in this case, we’re putting some data into

  [00:27:36.387]
  the time. Siri’s overview.

  [00:27:38.948]
  This allows us to authenticate and send

  [00:27:41.097]
  data to that particular

  [00:27:43.178]
  Siri’s.

  [00:27:44.288]
  But we might also want to retrieve that data

  [00:27:46.307]
  as well. And there’s example calls for doing

  [00:27:48.528]
  that. It also

  [00:27:50.667]
  has some example pages to show

  [00:27:52.667]
  you how to build out things like graphs

  [00:27:55.178]
  and charting to showcase information

  [00:27:57.948]
  that is coming live from the actual

  [00:28:00.157]
  I OT devices.

  [00:28:03.847]
  Once you’re happy with this application, you

  [00:28:05.897]
  can then build a package for it

  [00:28:08.117]
  and then deploy it, using the command

  [00:28:10.528]
  line for cloud foundry

  [00:28:12.788]
  simply by looking in and given it

  [00:28:14.788]
  the actual package, it will build

  [00:28:16.847]
  you the application, deploy into the

  [00:28:18.887]
  minds of platform,

  [00:28:20.201]
  allow you to run that and use it within

  [00:28:22.481]
  your environment.

  [00:28:28.781]
  So in the last 30 minutes or so, we’ve been out

  [00:28:30.961]
  to show you how to build an

  [00:28:33.182]
  application on AWS I

  [00:28:35.372]
  ot We’ve also shown you

  [00:28:37.412]
  how to build an example application

  [00:28:40.011]
  using mind Sphere platform on

  [00:28:42.271]
  Bendix

  [00:28:43.211]
  and giving you an overview as to what the

  [00:28:45.501]
  I ot landscape looks like. And

  [00:28:47.711]
  how men Dix come really support that

  [00:28:49.771]
  landscape.

  [00:28:51.642]
  So I hope you’ve enjoyed this particular weapon

  [00:28:53.682]
  are and I hope to see you are next

  [00:28:55.821]
  weapon are our next one will be

  [00:28:57.951]
  next month and will be focusing on

  [00:29:00.162]
  multi experience development on

  [00:29:02.162]
  how Mendez can build applications

  [00:29:04.832]
  that cross multiple experiences.

  [00:29:10.182]
  So we will now move on to the question and

  [00:29:12.231]
  answer section.