How to load incremental data using pyspark?

Let's illustrate the process of loading incremental data in a banking context using a simplified PySpark example with sample data. In this example, we'll work with transaction data and load new transactions into an existing dataset.

There are two files existing_data.csv and new_data.csv for that we have following pyspark code.

# Load existing transaction data
existing_data_df = spark.read \
  .format("csv") \
  .option("header", "true") \
  .load("dbfs:/FileStore/raw/existing_data.csv")

# Load new transaction data
new_data_df = spark.read \
  .format("csv") \
  .option("header", "true") \
  .load("dbfs:/FileStore/raw/new_data.csv")

existing_data_df.show()

# Identify new records based on timestamp
max_timestamp_existing = existing_data_df.selectExpr("max(timestamp)").collect()[0][0]
new_records_df = new_data_df.filter(new_data_df.timestamp > max_timestamp_existing)

# Append new records to existing data
updated_data_df = existing_data_df.union(new_records_df)

# Show the updated dataset
updated_data_df.show()

In this code:
We load the existing and new transaction data from CSV files into PySpark DataFrames.
We identify new records by comparing timestamps. In a real banking project, you might use more complex criteria, such as account numbers and transaction IDs.
We append the new records to the existing dataset.
Finally, we can write the updated dataset back to the target data store, or in this case, overwrite the existing CSV file.
Remember to adjust the code to your specific use case, including handling different data formats, database connections, and additional data validation and transformations as needed.

Note : We can parametrize file path for loading daily incremntal data accroding to the use case.