PySpark Transformations and Windows | Dataford Interview Questions

Describe how you would implement a solution using PySpark DataFrame transformations and SQL window functions.

Use the provided account and activity data to demonstrate the equivalent relational result. Include activity records from January 2025 and return one row per qualifying activity.

Output

Return account_id, account_name, record_id, record_date, amount, record_number, previous_amount, and cumulative_amount.
Number records per account chronologically, show the prior amount, and calculate the cumulative amount.
Order by account_id, record_date, and record_id ascending.

Schema

accounts

Column	Type	Description
account_idPK	INT	Unique account identifier
account_name	VARCHAR(100)	Display name for the account

activity_records

Column	Type	Description
record_idPK	INT	Unique activity record identifier
account_id	INT	Account associated with the activity
record_date	DATE	Date on which the activity occurred
amount	DECIMAL(10,2)	Activity amount, which may be NULL

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