Run and schedule a pipeline

To run and schedule a pipeline for the exam “Designing and Implementing a Data Science Solution on Azure,” you can use various Azure services and tools. In this article, we’ll explore how to set up and automate a data science pipeline using Azure Data Factory, Azure Databricks, and Azure DevOps.

1. Setting up Azure Data Factory

Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create, schedule, and manage data workflows. Follow these steps to set up ADF:

1. Create an Azure Data Factory resource in the Azure portal.
2. Create a data pipeline in ADF by defining the source and destination datasets, activities, and transformations required for your data science solution.

2. Integrating Azure Databricks with Azure Data Factory

Azure Databricks is an Apache Spark-based analytics platform that provides a collaborative environment for data science and machine learning. To integrate Databricks with ADF, follow these steps:

1. Create an Azure Databricks workspace in the Azure portal.
2. In ADF, create a new Linked Service for Databricks, providing the necessary connection details.
3. Use the Databricks activity in ADF pipelines to run notebooks or jobs in the Databricks workspace. This enables executing data transformation and model training tasks using Databricks.

3. Automating the Pipeline with Azure DevOps

Azure DevOps is a set of development tools that provides CI/CD capabilities for building, testing, and deploying applications. To automate the data science pipeline, follow these steps:

1. Set up a code repository (e.g., Azure Repos) to store pipeline definitions and scripts.
2. Define a YAML pipeline in Azure DevOps, specifying the tasks required to run the data science solution.
3. Add appropriate tasks for data ingestion, transformation, model training, and evaluation using Azure CLI, Azure PowerShell, or other Azure DevOps extensions.
4. Configure triggers to schedule the pipeline execution at regular intervals or trigger it manually.

Here’s an example YAML pipeline configuration for your reference:

trigger:
branches:
include:
- main

steps:
- task: AzurePowerShell@5
inputs:
azureSubscription: 'YourAzureSubscription'
scriptType: 'InlineScript'
scriptLocation: 'InlineScript'
inlineScript: |
# Azure CLI commands to start the ADF pipeline
az datafactory pipeline create-run -g -f -p

Once you’ve set up the pipeline, you can run and schedule it using Azure DevOps. You can also monitor the pipeline execution, track logs, and get notified about any failures or issues.

By following the steps outlined above, you’ll be able to run and schedule a data science pipeline for the “Designing and Implementing a Data Science Solution on Azure” exam. Azure Data Factory (ADF), Azure Databricks, and Azure DevOps provide the necessary tools and capabilities to automate your data science workflows effectively.