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How to setup an end-to-end solution to implement a streaming at scale scenario using a choice of different Azure technologies.

Streaming at Scale

The samples 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:

Big Data Architectures: notes on Kappa, Lambda and IoT streaming architectures
Real Time Processing: details on real-time processing architectures

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:

Choosing a stream processing technology in Azure

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

Running the samples

Please note that the scripts have been tested on Ubuntu 18 LTS, so make sure to use that environment to run the scripts. You can run it using Docker, WSL or a VM:

Just do a git clone of the repo and you'll be good to go.

Each sample may have additional requirements: they will be listed in the sample's README.

Streamed Data

Streamed data simulates an IoT device sending the following JSON data:

{
    "eventId": "b81d241f-5187-40b0-ab2a-940faf9757c0",
    "complexData": {
        "moreData0": 57.739726013343247,
        "moreData1": 52.230732688620829,
        "moreData2": 57.497518587807189,
        "moreData3": 81.32211656749469,
        "moreData4": 54.412361539409427,
        "moreData5": 75.36416309399911,
        "moreData6": 71.53407865773488,
        "moreData7": 45.34076957651598,
        "moreData8": 51.3068118685458,
        "moreData9": 44.44672606436184,
        [...]
    },
    "value": 49.02278128887753,
    "deviceId": "contoso-device-id-000154",
    "deviceSequenceNumber": 0,
    "type": "CO2",
    "createdAt": "2019-05-16T17:16:40.000003Z"
}

Duplicate event handling

Event delivery guarantees are a critical aspect of streaming solutions. Azure Event Hubs provides an at-least-once event delivery guarantees. In addition, the upstream components that compose a real-world deployment will typically send events to Event Hubs with at-least-once guarantees (i.e. for reliability purposes, they should be configured to retry if they do not get an acknowledgement of message reception by the Event Hub endpoint, though the message might actually have been ingested). And finally, the stream processing system typically only has at-least-once guarantees when delivering data into the serving layer. Duplicate messages are therefore unavoidable and are better dealt with explicitly.

Depending on the type of application, it might be acceptable to store and serve duplicate messages, or it might desirable to deduplicate messages. The serving layer might even have strong uniqueness guarantees (e.g. unique key in Azure SQL Database). To demonstrate effective message duplicate handling strategies, the various solution templates demonstrate, where possible, effective message duplicate handling strategies for the given combination of stream processing and serving technologies. In most solutions, the event simulator is configured to randomly duplicate a small fraction of the messages (0.1% on average).

Integration tests

End-to-end integration tests are configured to run. You can check the latest closed pulled requests ("View Details") to navigate to the integration test run in Azure DevOps. The integration test suite deploys each solution and runs verification jobs in Azure Databricks that pull the data from the serving layer of the given solution and verifies the solution event processing rate and duplicate handling guarantees.