Data Engineering on Microsoft Azure: Data Flow Transformations
Azure 2021
| Intermediate
- 11 Videos | 1h 23m 25s
- Includes Assessment
- Earns a Badge
One of the key components of the Azure Cloud platform is the ability to store and process large amounts of data. Azure Data Flow Transformations can be used to ingest and transform data. In this course, you'll learn about the types of Azure Data Flow transformations that are available. You'll explore how to transform, split, and flatten data, as well as handle duplicate data, using Azure Data Mapping Data Flows. Next, you'll examine the types of expression functions available in Azure Data Flow and how to perform error handling for data rows that would truncate data. Finally, you'll learn how to transform and use derived columns to normalize data values, and how to ingest and transform data using Azure Spark and Scala. This course is one in a collection that prepares learners for the Microsoft Data Engineering on Microsoft Azure (DP-203) exam.
WHAT YOU WILL LEARN
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discover the key concepts covered in this coursedescribe the type of available Azure Data Flow transformationstransform data using Azure Data Mapping Data Flowstransform and split data using Azure Data Flowtransform and flatten data using Azure Data Flowdescribe the types of expression functions available in Azure Data Flow
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configure Azure Data Flow to perform error handling for data rows that would truncate datatransform and use derived columns to normalize data valuesingest and transform data using Azure Spark and Scalahandle duplicate data using Azure Data Flowssummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 46sUP NEXT
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2.Azure Data Flow Transformations7m 40s
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3.Using Azure Data Mapping Data Flows9m 6s
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4.Performing an Azure Data Flow Conditional Split10m 50s
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5.Using Azure Data Flow Conditional Flatten8m 17s
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6.Azure Data Flow Expression Functions5m 26s
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7.Configuring Azure Data Flow Error Handling10m 3s
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8.Transforming Azure Data Flow Derived Columns10m 16s
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9.Transforming Data Using Azure Spark and Scala9m 56s
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10.Handling Duplicate Data in Azure Data Flows8m 46s
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11.Course Summary1m 19s
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