ISA-005 — Date Standardization

🟢 Beginner

⏱ 15 Minutes

⭐ 10 Points

🔓 Free

💻 SQL | PySpark

🏢 Retail Analytics

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Business Context

A retail company receives customer signup data from multiple regional systems.

Unfortunately, different systems send dates in different formats.

This inconsistency is causing reporting failures and dashboard refresh issues.

The analytics team needs all signup dates standardized before loading them into the reporting layer.

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Business Impact

Inconsistent date formats can lead to:

• Incorrect reporting
• Failed ETL jobs
• Broken dashboard filters
• Data quality issues

Standardizing dates ensures all downstream systems interpret dates consistently.

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Dataset

customer_signups

customer_id	customer_name	signup_date
101	John	01/15/2025
102	Alice	02/20/2025
103	Bob	03/05/2025
104	Sarah	04/12/2025
105	David	05/30/2025

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Task

Convert all signup dates into:

YYYY-MM-DD

format.

Return all customer records.

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Expected Output

expected_output

customer_id	customer_name	signup_date
101	John	2025-01-15
102	Alice	2025-02-20
103	Bob	2025-03-05
104	Sarah	2025-04-12
105	David	2025-05-30

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Constraints

• All dates are valid.
• Convert every date to YYYY-MM-DD format.
• Return all customer records.

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Supported Languages

✅ SQL

✅ PySpark

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Hint

Parse the incoming date format before converting it to YYYY-MM-DD.

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Solution

🔒 Premium Solution

Premium members receive:

• SQL Solution
• PySpark Solution
• Step-by-Step Explanation
• Optimization Discussion

Notebook Workspace

Cell 1Dataset: customer_signups

⏱ 15:00

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Run Results

✓ Workspace Ready

Rows Returned: --

Execution Time: --

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