Data Engineering on Microsoft Azure: Databrick Processing

Azure    |    Intermediate
  • 11 videos | 1h 49m 12s
  • Includes Assessment
  • Earns a Badge
When working with big data there needs to be a mechanism to process and transform this data quickly and efficiently. Azure Databricks is a service that provides the latest version of Apache Spark that provides functionality processing data from Azure Storage. In this course, you will learn about the types of processing that can be performed with Azure Databricks such as stream, batch, image and parallel processing. Next, you'll learn how to create an Azure Databricks workspace using an Apache Spark cluster, run jobs in the Azure Databricks Workspace jobs using a service principal and query data in SQL server using an Azure Databricks notebook. Next, you'll learn how to retrieve data from an Azure Blob Storage using Azure Databricks and the Azure Key Vault, implement a Cosmos DB service endpoint for Azure Databricks, and extract, transform, and load data using Azure Databricks. Finally, you'll learn how to stream data into Azure Databricks by using Event Hubs and perform sentiment analysis for steam data by making use of Azure Databricks. This course is one in a collection that prepares learners for the Microsoft Data Engineering on Microsoft Azure (DP-203) exam.


  • Discover the key concepts covered in this course
    Describe the types of available processing when using azure databricks such as stream, batch, image and parallel processing
    Create an azure databricks workspace using an apache spark cluster
    Run jobs in the azure databricks workspace jobs using a service principal
    Query data in sql server using an azure databricks notebook
    Validate and handle failed batch loads
  • Implement a cosmos db service endpoint for azure databricks
    Extract, transform, and load data using azure databricks
    Perform sentiment analysis for steam data by making use of azure databricks
    Debug spark jobs running on hdinsight
    Summarize the key concepts covered in this course


  • 2m 7s
  • 8m 9s
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    3.  Creating an Azure Databricks Workspace
    5m 58s
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    4.  Running Azure Databricks Workspace Jobs
    21m 8s
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    5.  Querying SQL Server
    12m 25s
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    6.  Failed Batch Loads
    4m 40s
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    7.  Implementing Cosmos DB Endpoints
    17m 59s
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    8.  Extracting, Transforming, and Loading Data
    10m 31s
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    9.  Performing Sentiment Analysis
    17m 10s
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    10.  Debugging Spark Job
    8m 11s
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    11.  Course Summary
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