The first snippet of code imports the explode() and col() methods from the pyspark.sql.functions class. Then the JSON file is loaded into a DataFrame with an option stipulating that the file is multiline as...
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Encode and Decode Data– Transform, Manage, and Prepare Data
The output is SQL_Latin1_General_CP1_CI_AS, which is the default (refer to Figure 3.28). GO INSERT INTO [dbo].[ENCODE] ([ENCODE_ID], [ENCODE]) VALUES (1, ‘殽’)INSERT INTO [dbo].[ENCODE] ([ENCODE_ID], [ENCODE]) VALUES (2, ‘Ž’)INSERT INTO [dbo].[ENCODE] ([ENCODE_ID], [ENCODE]) VALUES (3,...
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Transform Data Using Apache Spark—Azure Synapse Analytics – Transform, Manage, and Prepare Data-1
Transform Data Using Apache SparkApache Spark can be used in a few products running on Azure: Azure Synapse Analytics Spark pools, Azure Databrick Spark clusters, Azure HDInsight Spark clusters, and Azure Data Factory. The...
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Transform Data Using Azure Synapse Pipelines – Transform, Manage, and Prepare Data-3
One action you may have noticed in Exercise 5.1 is that you used the existing pipeline that you created in Exercise 4.13. That pipeline performed one activity, which was to copy data from the...
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Storing Prepared, Trained, and Modeled Data – Data Sources and Ingestion
All data, regardless of the Big Data stage it is in, must be stored. The data not yet ingested into the pipeline is stored someplace too, but not yet on Azure. You can use...
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