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Data Engineering with Apache Spark, Delta Lake, and Lakehouse

You're reading from  Data Engineering with Apache Spark, Delta Lake, and Lakehouse

Product type Book
Published in Oct 2021
Publisher Packt
ISBN-13 9781801077743
Pages 480 pages
Edition 1st Edition
Languages
Author (1):
Manoj Kukreja Manoj Kukreja
Profile icon Manoj Kukreja
Toc

Table of Contents (17) Chapters close

Preface 1. Section 1: Modern Data Engineering and Tools
2. Chapter 1: The Story of Data Engineering and Analytics 3. Chapter 2: Discovering Storage and Compute Data Lakes 4. Chapter 3: Data Engineering on Microsoft Azure 5. Section 2: Data Pipelines and Stages of Data Engineering
6. Chapter 4: Understanding Data Pipelines 7. Chapter 5: Data Collection Stage – The Bronze Layer 8. Chapter 6: Understanding Delta Lake 9. Chapter 7: Data Curation Stage – The Silver Layer 10. Chapter 8: Data Aggregation Stage – The Gold Layer 11. Section 3: Data Engineering Challenges and Effective Deployment Strategies
12. Chapter 9: Deploying and Monitoring Pipelines in Production 13. Chapter 10: Solving Data Engineering Challenges 14. Chapter 11: Infrastructure Provisioning 15. Chapter 12: Continuous Integration and Deployment (CI/CD) of Data Pipelines 16. Other Books You May Enjoy

Running the aggregation pipeline

In production, the aggregation pipeline will be invoked automatically once the curation pipeline completes successfully. Since we are in the unit testing phase, we will trigger it manually for now. Let's get started:

  1. You must invoke the following commands on Azure Cloud Shell. They will invoke electroniz_aggregation_pipeline based on the parameters we pass. Please make sure you edit EXT_TAB_LOCATION as per the instructions provided. Using the Azure portal, navigate to your Azure Data Lake Storage account by going to Home > All Resources > traininglakehouse > Endpoints.

    Note the URL of Blob Service and use this to edit EXT_TAB_LOCATION:

    RESOURCEGROUPNAME="training_rg"
    DATAFACTORYNAME="traininglakehousedf"
    PIPELINENAME="electroniz_batch_aggregation_pipeline"
    az datafactory pipeline create-run --factory-name $DATAFACTORYNAME --name $PIPELINENAME --resource-group $RESOURCEGROUPNAME \
    --parameters "...
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