DataFrames and Transformations

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Note: Azure Table and Queue Storage support NoSQL and message handling for scalable cloud apps

DataFrames and Transformations 

In PySpark, DataFrames are the primary abstraction for working with structured and semi-structured data. A DataFrame is a distributed collection of data organized into named columns, similar to a table in a relational database or a Pandas DataFrame but optimized for big data processing.

You can create DataFrames from various sources such as CSV, JSON, Parquet, Hive tables, or RDDs. The SparkSession object is used to create and manipulate DataFrames.

Transformations are operations that produce a new DataFrame from an existing one. They are lazy, meaning Spark doesn’t execute them until an action is called. Common transformations include:

select() – chooses specific columns

filter() or where() – filters rows based on conditions

groupBy() – groups data for aggregation

withColumn() – adds or modifies columns

drop() – removes columns

join() – combines two DataFrames

Transformations can be chained together to form complex data pipelines. Since they are lazy, Spark optimizes the execution plan before running the transformations.

Actions like show(), collect(), count(), or write() trigger the actual computation and return results or write them to external storage.

Using DataFrames and transformations is efficient and expressive, making PySpark suitable for large-scale data processing and analytics.

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