Now that you have performed some data transformation exercises, it is a good time to read about some applicable transformation and data management concepts. Transformation As you progressed through the exercises that transformed the...
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Normalize and Denormalize Values– Transform, Manage, and Prepare Data
Notice that the input for normalization is the same as the data shown in the previous data table. The aspect of the resulting normalized data you may notice first is the existence of the...
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Normalize and Denormalize Values– Transform, Manage, and Prepare Data
Normalization and denormalization can be approached in two contexts. The first context has to do with the deduplication of data and query speed on database tables in a relational database. The other context has...
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Configure Error Handling for the Transformation– Transform, Manage, and Prepare Data
As you transform data using Azure Synapse Analytics, there may be some failures when writing to the sink. The failures might happen due to data truncation, such as when the data type is defined...
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Machine Learning– Transform, Manage, and Prepare Data
There are many techniques to consider when you want to better format data values for machine learning—or learning in general. Having data optimally organized increases the machine learning algorithm’s ability to efficiently predict and...
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Transform Data by Using Scala– Transform, Manage, and Prepare Data
In Exercise 5.4 you used the Scala language to perform data transformation. You received a data file in Parquet format, transformed it to a more queryable form, and stored it in a delta lake....
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Perform Exploratory Data Analysis—Transform– Transform, Manage, and Prepare Data
This exercise requires a Power BI Premium subscription, which can be acquired at https://powerbi.microsoft.com. FIGURE 5.32 Performing exploratory data analysis—visualizing data in Power BI (2) FIGURE 5.33 Performing exploratory data analysis—Power BI workspace FIGURE...
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