Streaming at Scale

Sample end-to-end solutions to implement streaming at scale scenarios using Azure

About the repository

The sample shows how to setup an end-to-end solution to implement a streaming at scale scenario using a choice of different Azure technologies. There are many possible way to implement such solution in Azure, following Kappa or Lambda architectures, a variation of them, or even custom ones. Each architectural solution can also be implemented with different technologies, each one with its own pros and cons.

More info on Streaming architectures can also be found here:

Here's also a list of scenarios where a Streaming solution fits nicely

A good document the describes the Stream Technologies available on Azure is the following one:

Choosing a stream processing technology in Azure

The goal of this repository is to showcase all the possible common architectural solution and implementation, describe the pros and the cons and provide you with sample script to deploy the whole solution with 100% automation.

Running the samples

All samples uses AZ CLI and Bash scripts. Make sure you have AZ CLI installed:

If you're running on Windows, it is suggested to run script from WSL

although you can also run them from any Bash environment. Just keep in mind that script have been tested on Ubuntu on WSL and OS X only.

In order to clone the repository you'll also need Git:

The Git For Windows version comes with a Bash too

Some samples may have more specific needs. In that case the required software will be mentioned in sample's readme.

Available solutions

At present time the available solutions is

Cosmos DB Sample

Implement a stream processing architecture using: - EventHubs (Ingest / Immutable Log) - AzureFunctions (Stream Process) - Cosmos DB (Serve)

EventHubs Capture Sample

Implement stream processing architecture using: - EventHubs (Ingest) - EventHubs Capture (Store) - Azure Blob Store (Data Lake) - Apache Drill (Query/Serve)

Stream Analytics Sample

Implement a stream processing architecture using: - EventHubs (Ingest / Immutable Log) - Stream Analytics (Stream Process) - Cosmos DB (Serve)


The following technologies are planned to be used in the end-to-end sample solution


  • IoT Hub (Work in Progress)
  • EventHub Kafka

Stream Processing

  • Databricks Spark Structured Streaming
  • Azure Stream Analytics
  • Azure Functions
  • Azure Data Explorer

Batch Processing

  • EventHubs Capture
  • Databricks Spark
  • Azure Data Explorer
  • Open Source solutions (like Apache Drill)

Serving Layer

  • Azure Data Explorer
  • Cosmos DB
  • Azure SQL
  • Azure DW