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System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

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This is a medium difficulty System Design question, commonly asked during Onsite rounds at Netflix.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Netflix during technical interviews.

Model ads demand data for reporting | Netflix Interview Question

Q: Model ads demand data for reporting

This question evaluates competencies in data modeling, ETL/ELT pipeline design, analytics warehousing, handling slowly changing dimensions, and high-volume event processing in the System Design domain.

You are asked to design the data model (and supporting pipelines at a high level) to represent ads demand for an ads platform.

“Demand” here refers to what advertisers want to buy/spend, and what inventory they are eligible to serve on.

Requirements

Support reporting for:
- Booked budget, remaining budget, and spend to date
- Delivery (impressions, clicks, conversions)
- Pacing vs time (e.g., under/over-delivering)
- Breakdown by advertiser, campaign, ad group/line item, creative, targeting, geo, device, day
Handle campaign lifecycle changes:
- Budget edits, start/end date edits, pause/resume
- Creative swaps and targeting updates
Data sources (assume): impression/click logs (high volume), conversion events, campaign configuration service (slow-changing)
Output: propose a warehouse schema (tables + keys), and outline ETL/ELT steps and key validations.

Deliverables

Core entities and relationships
Fact and dimension tables (grain matters)
How you track slowly changing campaign configuration
Example queries/KPIs the model supports

You are asked to design the data model (and supporting pipelines at a high level) to represent ads demand for an ads platform.

“Demand” here refers to what advertisers want to buy/spend, and what inventory they are eligible to serve on.

Requirements

Support reporting for:
- Booked budget, remaining budget, and spend to date
- Delivery (impressions, clicks, conversions)
- Pacing vs time (e.g., under/over-delivering)
- Breakdown by advertiser, campaign, ad group/line item, creative, targeting, geo, device, day
Handle campaign lifecycle changes:
- Budget edits, start/end date edits, pause/resume
- Creative swaps and targeting updates
Data sources (assume): impression/click logs (high volume), conversion events, campaign configuration service (slow-changing)
Output: propose a warehouse schema (tables + keys), and outline ETL/ELT steps and key validations.

Deliverables

Core entities and relationships
Fact and dimension tables (grain matters)
How you track slowly changing campaign configuration
Example queries/KPIs the model supports

Model ads demand data for reporting

Quick Overview

Requirements

Deliverables

Solution

Comments (0)

Model ads demand data for reporting

Quick Overview

Requirements

Deliverables

Solution

Comments (0)