Schema mapping definition:

Schema mapping is the process of mapping the schema of one dataset to the schema of another dataset in order to integrate or merge them. It involves mapping the fields or attributes of one dataset to the fields or attributes of another dataset, ensuring that the data types, formats, and other properties match between the two datasets. Schema mapping is a crucial step in data integration, as it enables datasets with different schemas to be combined and analyzed together.

Schema mapping example using Python:

Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.

In Python, one way to perform schema mapping is by using the PySpark library. Here's an example:

# Importing the required libraries
from pyspark.sql import SparkSession
from pyspark.sql.functions import col

# Creating a SparkSession
spark = SparkSession.builder.appName("Schema Mapping Example").getOrCreate()

# Creating a sample dataframe with multiple columns
data = [("John", 25, "25000.50"), ("Smith", 30, "35000.75"), ("Jane", 28, 32000.25)]
df = spark.createDataFrame(data, ["name", "age", "salary"])

# Defining the mapping schema
mapping_schema = {
    "name": "string",
    "age": "integer",
    "salary": "double"
}

# Creating a new dataframe with the mapped schema
mapped_df = df.select([col(c).cast(mapping_schema[c]).alias(c) for c in mapping_schema.keys()])

# Displaying the original and mapped dataframes
print(f"Original dataframe: {df}")
print(f"Mapped dataframe: {mapped_df}")

# Applying some transformations on the mapped dataframe
filtered_df = mapped_df.filter(mapped_df.age > 25)
grouped_df = filtered_df.groupBy("name").agg({"salary": "sum"})

# Stopping the SparkSession
spark.stop()

This will print out the schema of each dataframe showing the new mapping:

Original dataframe: DataFrame[name: string, age: bigint, salary: string]
Mapped dataframe: DataFrame[name: string, age: int, salary: double]