Experience the basics of Azure IoT, dashboarding … and beyond

Internet of Things (IoT) is hot and happening. Many companies benefit from their machine data by optimizing their processes with remote monitoring and predictive maintenance.

,,But I don't know anything about IoT. How should I start? How do I get value from incoming raw telemetry?''

The best way to learn it is to experience it!

Within this half-day workshop, you'll follow the scenario in which a remote coffee machine is connected to Azure for monitoring and management of issues. You can monitor device telemetry such as water temperature and water level, observe the state of your machine, set the optimal temperature, and send commands. Next to that, you learn how to query the data using KQL and use that for building simple but effective dashboards.

With this introduction, you will get an idea of the basic IoT principles by hands-on experience with a simulated device, Azure IoT Hub, Azure Data Explorer and Visual Studio Code. So, bring your laptop and prepare yourself for future IoT related cases and telemetry exploration within your organization.

You will turn your laptop into a coffeemaker. Check out the prerequisites at https://github.com/sandervandevelde/Experience-the-basics-of-Azure-IoT/

Ingest, query and visualize IoT streams using Azure Data Explorer

Telemetry and events, coming from IoT devices, ingested as a stream, must first be filtered, enriched, and aggregated before any value can be offered.

On top of that, these signals can be corrupted due to misconfiguration, faulty sensors, corrupted communication, or tampering. Data can even become missing due to lost connections or missing sensors, so you must fill in the gaps based on... nothing?

This is the burden of IoT Developers: one leg in that cosy cloud, one leg in the cruel physical world.

Luckily, The Azure cloud offers several tools to monitor that vast amount of telemetry for anomalies or trends.

Azure Data Explorer (ADX) is the new kid on the block for many IoT developers.

In this session, the attendees are introduced to the Azure IoT Hub Connection for ADX and they are explained how to get around common ingestion pitfalls.

Once telemetry arrives in ADX, attendees will learn about the Kusto Query Language (KQL), used to query all data and they will see how to visualize it in eg. integrated dashboard with multiple charts or Managed Grafana.

Presentation is packed with demos

Introduction to Azure Data Explorer for IoT Developers

Adding value using Internet of Things depends solely on the insights and actions being created, based on both raw and enriched telemetry.

Azure Data Explorer, this official successor of Time Series Insights brings these highly anticipated insights to your IoT platform.

In this session, we focus on how to implement the warm and cold path using Azure Data Explorer based on telemetry and potentially other data.

We will learn how to set up a cluster and create a database and tables. We learn about the different ways to connect and ingest telemetry in a smart way, and how to query the data using the Kusto Query Language.

We will also dive into different ways of building dashboards on top of the data ingested.

After the session, you have learned how to get started with Azure Data Explorer and how it keeps adding value during the full lifetime of your IoT projects.

This is the extended version of this presentation https://youtu.be/YcpAzAj-eRw?t=4170

Model your real-time metaverse with Azure Digital Twins

Digital Twins are the counterparts of devices, vehicles, places, etc., and are living in the cloud. The real world is modeled in a simplified way based on live data. This is the metaverse for the Internet of Things.

Every (IoT) platform supports different personas having their own use cases. Some personas only care about high-level KPIs while others are only interested in the raw data. And there are those interested in Digital Twins due to their support for rule-based decision-making.

Using Digital Twins, these users can make fast decisions based on the real-time data available in the system, enriched with historical insights and predictions toward the future. A 3D situation overview offers the most immersive experience.

In this session, attendees learn about how they can start with Azure Digital Twins, how to traverse the model graph, how business rules are implemented, and how Digital Twins can be represented in 3D without any extra programming.

This is the extended version of this presentation https://youtu.be/YcpAzAj-eRw?t=6332

Fancy some secure device toppings on your cloud-enabled vanilla MQTT broker?

Do you want to connect many IoT devices all over the world so they can communicate with each other in real time? Do you want to use standard MQTT... in a secure way?

On popular demand, one of the most popular IoT protocols is now offered as a native cloud service.

While IoT is maturing, Microsoft is expanding its Azure IoT proposition in multiple directions. This vanilla MQTT broker also comes with extra options to make it even more secure.

Using multiple demos, solutions with both device-to-device communication between actual industrial devices and device-to-cloud communication will be explained.

Next to the EventGrid namespace MQTT support, the integration with eg. Microsoft Fabric is demonstrated.

Learn why this new broker offers new advantages for your IoT solution, where the pitfalls are, and how it fit in your architecture to provide insights and value using real-time analytics.

This is a presentation filled with live demos where actual industrial hardware will be connected to the cloud

Attendees learn how the Azure services both honor vanilla MQTT and offer more elaborate services on top of it.

Cross the streams for Microsoft Fabric Real-Time Analytics

Are you already working with Real-Time data in your cloud platform?

This is no longer the domain of IoT, every data engineer will encounter streams of data to manage. Crossing the streams adds value!

Next to batch processing, Microsoft Fabric includes Real-Time Analytics where continuous flows of TimeSeries data can be ingested, queried, and turned into actions.

Attendees will learn how to set up real-time data stream ingestion and process messages for filtering, aggregation, or transformation. Storage of time-series data and querying using KQL is demonstrated, adding value to the raw data. Activating your data into automatic notifications based on basic rules, is also covered.

Tools like KQL databases, KQL Queryset, Eventstreams and Data Activator and more will be demonstrated.

Expect a demo-packed presentation for both data engineers and IoT developers.

This presentation focuses on the Real-Time Analytics part of Microsoft Fabric. See also my well-received 10-part blog series about this topic.