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This question is commonly asked for Data Engineer candidates at Meta during technical interviews.

Design tables for event-driven metrics | Meta Interview Question

Quick Overview

This question evaluates relational data modeling and data engineering competencies for high-volume event analytics, including schema design, partitioning and clustering strategies, duplicate and late-arriving event handling, and normalization versus denormalization trade-offs.

Design a Relational Schema for Consumer-App Event Analytics

Context and Assumptions

You are designing the event store for a high-volume consumer app. The schema must support common product analytics and growth metrics efficiently, including:

Activity: DAU/WAU/MAU, stickiness
Retention cohorts (N-day, week-over-week)
Sessions: counts and lengths
Funnels and conversions (e.g., view → add_to_cart → purchase)
Revenue (ARPU/ARPPU), feature usage, geo/device/app-version breakdowns

Assume at-least-once delivery from clients/services, possible late-arriving events, and high cardinality in event parameters. The store will be queried in a columnar data warehouse or relational engine that supports partitioning and clustering.

Task

Design a relational schema to store user events so that the metrics above can be computed efficiently. Provide:

Table definitions with columns and data types
Primary and foreign keys
Partitioning and indexing strategy
Sample rows
Explanations for normalization versus denormalization choices
How to handle late-arriving or duplicated events
How to support backfills and incremental fact tables

Quick Overview

Context and Assumptions

You are designing the event store for a high-volume consumer app. The schema must support common product analytics and growth metrics efficiently, including:

Activity: DAU/WAU/MAU, stickiness

Retention cohorts (N-day, week-over-week)

Sessions: counts and lengths

Funnels and conversions (e.g., view → add_to_cart → purchase)

Revenue (ARPU/ARPPU), feature usage, geo/device/app-version breakdowns

Task

Design a relational schema to store user events so that the metrics above can be computed efficiently. Provide:

Table definitions with columns and data types

Primary and foreign keys

Partitioning and indexing strategy

Sample rows

Explanations for normalization versus denormalization choices

How to handle late-arriving or duplicated events

How to support backfills and incremental fact tables

Design tables for event-driven metrics

Quick Overview

Design a Relational Schema for Consumer-App Event Analytics

Context and Assumptions

Task

Solution

Comments (0)

Design tables for event-driven metrics

Quick Overview

Design a Relational Schema for Consumer-App Event Analytics

Context and Assumptions

Task

Solution

Comments (0)