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Azure-Big-Data-and-Machine-Learning-Architecture

A ready to use architecture for processing data and performing machine learning in Azure

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What it does

  1. Creates all the necessary Azure resources
  2. Wires up security between resources
  3. Allows you to upload data as thought you are a customer (SAMPLE-End-Customer-Upload-To-Blob.{ps1 or sh})
    1. An event from the upload will trigger a data factory to move data from the landing storage account to the data lake
  4. There is a data factory that will download NYC Taxi data (you execute the pipeline ProcessNYCTaxiData by hand)

    1. (This is being worked on!) A Data Flow will move the data from the landing zone on the data lake to the raw "bronze" zone (it will convert the files to parquet)

    2. A Databricks notebook will then create reference data tables in the raw zone.

    3. A Data Flow will move the data from the raw zone to the transformed "silver" zone (it will add reference data)

    4. A Data Flow will move the data from the transformed zone to the enriched "gold" zone (it will place the data in the ready to use format)

    5. A Data Flow will move the data from the enriched/gold zone to the modeled zone (it will place the data in a b-star schema)

      1. SQL OD will load the data from the modeled zone to an Azure Analysis Service cube
      2. A SQL Hyerscale database will be loaded with the modeled data

How to run

Prerequisites

  1. Install PowerShell: https://docs.microsoft.com/en-us/powershell/azure/install-az-ps?view=azps-3.7.0
  2. Install Visual Studio (to review the code - the goal is to have a devops deployment, right now you publish the Azure Function by hand)

Running

  1. Clone this repo to your local computer (you can fork if you want)
  2. Fork the https://github.com/AdamPaternostro/Azure-Big-Data-and-Machine-Learning-Architecture-ADF to a GitHub account
  3. Replace the string "00005" with something else in lowercase e.g. "00099" withing all the downloaded files (hint: use VS Code or something). This will generate unique Azure names.
  4. Run STEP-01-CreateResourceGroupAndServicePrinciple.ps1 (must be an Subscription admin)
  5. Run STEP-02-Deploy-ARM-Template.ps1 (uses service principal above)
  6. Run STEP-03-InitializationScript.ps1 (must be an Subscription admin, at least until the service principal gets correct permissions set)

Copy Sample Taxi Data

  • Open the data factory
  • Authorize Azure to talk to your GitHub
  • Run the pipeline: ProcessNYCTaxiData

Upload Sample Data (as though you are a customer)

  • Publish the Azure Function (right click in Visual Studio and click Publish)
  • Open the SAMPLE-End-Customer-Upload-To-Blob.{ps1 or sh}
    • Change the Azure Function "code" line: $azureFunctionCode="baBqKrKC97HA/sLvZvjHtxCq82a43UmevfNSOwJU9DSuUXt6dUAixA==". You get this from the Azure Portal and click on the function GetAzureStorageSASUploadToken and the click the "</> Get function URL" and copy the code.
  • Run the sample
    • You should see the script generate a file and upload it
    • An end_file.txt will be generated and uploaded
    • The script will complete
    • A queue in the landing storage account named "fileevent" should get an item in it
    • The Azure Function will run every 5 minutes and pickup the queue item
    • The Azure Function will kick off the ADF Pipeline CopyLandingDataToDataLake
    • The ADF pipeline will copy the data from the landing storage account to the data lake.

Ideas

  • Use Azure Data Share to transfer files from customer that have an Azure subsription. This eliminates the need for the customer to perform an upload process.

Task List

Coding

  • Azure Function that processes the AAS cube (Jeremy)

Azure DevOps

  • Multistage templates

Samples

  • Sample generator program to generate streaming data for streaming pattern
  • Sample databricks notebooks for procssing
  • Sample Data flows for processing
  • Sample data wragling for processing
  • Load SQL DW using ADF
  • SQL DW / SQL Server create tables (DACPAC)
  • Create Hive Tables
  • Process ML model
  • FTP would be good to include

Security

  • Trying to use MSI for everything!
  • Create service principle only if needed (so far just one to deploy this)
  • Databricks could use Scopes for secrets
  • Could use KeyVault for secrets (if so then access using MSI)

Notes

  • create key vault policy via arm

Adam Tasks

  • Moving data
  • use SQL OD to load AAS
  • AAS needs full SDK for Azure Function v1 (does v1 support durrble functions)
  • Use NYC taxi data (over time there is schema drift)
  • Sample Data
    • read with Spark (or data flows)
    • do few joins
    • do partitions
    • result: Partitioned data
  • Customer could upload data and I can merge into the Sample Data set and process cube