Question:
Let's consider a case where you're dealing with an IoT project. You're receiving huge amounts of telemetry data from IoT devices, and this data should trigger an Azure function that analyses the data in near real-time. Assuming you would need a service that can handle vast amounts of data with millisecond response times, storage should provide low latency at any scale. How would you structure the Azure function for this situation? Please provide the triggers and bindings in the given code.
[FunctionName("ProcessTelemetryData")]
public static void Run(
/* Triggers and Bindings here */
ILogger log)
{
// Loop over the events array
// For each event, process the telemetry data
// Write the processed data to storage
}
Answer:
Event Hubs would again be the right choice for this scenario, given their capability to handle large amounts of data in real-time. The telemetry data could be sent to an Event Hub, which would trigger the function. The function could then analyze the data and use an output binding to store the analysis results. Cosmos DB's capabilities to provide low latency at any scale and its global distribution n
[FunctionName("ProcessTelemetryData")]
public static void Run(
[EventHubTrigger("<event-hub-name>", Connection = "EventHubConnectionString")]
EventData[] events,
[CosmosDB("<database-name>", "<collection-name>", ConnectionStringSetting = "CosmosDBConnectionString")]
out dynamic document,
ILogger log)
{
// Loop over the events array
// For each event, process the telemetry data
// Write the processed data to storage
}