Spark SQL – Select Columns From DataFrame
In Spark SQL, select() function is used to select one or multiple columns, nested columns, column by index, all columns,…
In Spark SQL, select() function is used to select one or multiple columns, nested columns, column by index, all columns,…
In Spark SQL, in order to convert/cast String Type to Integer Type (int), you can use cast() function of Column…
In PySpark SQL, using the cast() function you can convert the DataFrame column from String Type to Double Type or…
What is the difference between Spark map() vs flatMap() is a most asked interview question, if you are taking an…
The difference between Client vs Cluster deploy modes in Spark/PySpark is the most asked Spark interview question - Spark deployment mode…
Spark/PySpark partitioning is a way to split the data into multiple partitions so that you can execute transformations on multiple…
Let's learn what is the difference between PySpark repartition() vs partitionBy() with examples. PySpark repartition() is a DataFrame method that…
PySpark partitionBy() is a function of pyspark.sql.DataFrameWriter class which is used to partition the large dataset (DataFrame) into smaller files…
PySpark DataFrame has a join() operation which is used to combine fields from two or multiple DataFrames (by chaining join()),…
By using pyspark.sql.functions.pandas_udf() function you can create a Pandas UDF (User Defined Function) that is executed by PySpark with Arrow…