Real-Time Data Stream Processing In Azure

Learn how to ingest streaming data with Azure Event Hubs, including event producers, partitions, and consumer groups, and how capture to blob storage or data lake enables real-time analytics.

Provision an Azure event hub by creating a resource group and namespace, then an event hub with partitions, enable capture, and set message retention and consumer groups for real-time processing.

Learn to build a C# console lab that generates fake pump data, serializes it to JSON, and sends events to an Azure Event Hub using the Event Hub client.

Receive events from an event hub with a .NET console app using an event processor host, manage checkpoints in a storage account, and read 100 messages with UTF-8 encoding.

Explore event hub consumer groups to let multiple jobs read the same data independently, switch between default and job one views, and enable capture with partition and retention settings.

Set up a data lake, enable capture, and assign archive permissions to the event hub to stream test events into the lake, preserving the capture file structure and body data.

Learn how Azure Stream Analytics enables real-time analytics across multiple data streams from devices, sensors, websites, and apps, with inputs, queries, and outputs to databases, dashboards, and downstream services.

Build a real-time data pipeline in Azure: create an Event Hub and SQL Server, configure a Stream Analytics job with Event Hub input and SQL output, and stream JSON payloads.