I started with the advertiser and campaign hierarchy which felt natural, but then I got kind of lost deciding how granular to make the budget entity.
Start by clarifying requirements and scale, then design a normalized schema for core entities (advertisers, campaigns, ad groups, creatives) and a denormalized event schema for high-volume impressions, clicks, and conversions. Discuss trade-offs around consistency, partitioning, and budget tracking, and explain how you'd handle real-time budget enforcement and reporting.
Pro tip: Emphasize partitioning and indexing strategies for event tables (e.g., by date and advertiser) and mention using a separate service or cache for real-time budget checks to avoid hot spots. Show awareness of Netflix's scale by discussing sharding and eventual consistency for analytics.
Ask about expected QPS, data volume, latency requirements, and consistency needs for budget tracking vs. reporting. This shows you design for real constraints.
Define tables for advertisers, campaigns, ad groups, and creatives with primary/foreign keys and important attributes (e.g., status, targeting, budget). Normalize to avoid redundancy.
Create append-only event tables with timestamps, user/ad IDs, and foreign keys to creatives. Discuss partitioning (e.g., by date) and indexing for query performance.
Explain how to track spend per campaign/ad group using aggregated counters or a ledger, and how to enforce budgets in real-time (e.g., via a cache or stream processing) while handling concurrency.
Cover normalization vs. denormalization, SQL vs. NoSQL, consistency models, and sharding strategies. Mention how reporting can use a separate data warehouse or materialized views.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business metrics and dimensions, then propose a star schema with a fact table at the appropriate grain (e.g., ad impression or user session) and dimension tables for attributes like content, device, and geography. Explain how pre-aggregation and partitioning strategies can balance query performance with storage cost for analytical workloads.
Pro tip: Emphasize the trade-off between query flexibility and performance by discussing materialized views or aggregate tables for common dimension combinations, and mention how Netflix's scale would require partitioning and columnar storage.
Ask which specific metrics (CTR, spend, conversion rate) and dimensions (content type, device, region, time) are needed, and at what granularity. This ensures the schema supports the actual analytical queries.
Define the lowest level of detail for the fact table, such as each ad impression or user interaction, to allow flexible aggregation. Explain why this grain supports all required metrics.
Propose a central fact table with foreign keys to dimension tables (e.g., date, content, device, geography). Describe how this denormalized structure simplifies analytical queries and improves performance.
Discuss partitioning (e.g., by date), columnar storage, and pre-aggregated summary tables or materialized views for common queries. Mention how this balances query speed with storage and maintenance costs.
Explain how to track changes in dimensions (e.g., content metadata updates) using techniques like SCD Type 2, so historical analysis remains accurate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the ads platform's query patterns, data volume, and update frequency, then compare star and snowflake schemas on those dimensions. Conclude with a justified choice, typically star schema for analytics-heavy ad platforms, and note any hybrid considerations.
Pro tip: Emphasize that the choice depends on the workload: star schema simplifies queries and improves performance for BI, while snowflake reduces redundancy but adds join complexity—show you can quantify the trade-off.
Ask about query patterns (ad-hoc vs. predefined), data volume, ETL complexity, and normalization needs to ground the comparison.
Briefly define star (denormalized dimensions) and snowflake (normalized dimensions) to ensure shared understanding.
Contrast them on query performance, storage, ETL maintainability, and flexibility for ad platform metrics like impressions and clicks.
Choose star schema for its simplicity and performance in analytics, unless specific normalization needs justify snowflake.
Mention hybrid approaches or scenarios where snowflake might be preferred, showing nuanced thinking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about keeping transactional writes like budget updates and impression logging in a row-store, then syncing to a columnar warehouse for analytics.
Start by clarifying the system's read/write patterns and consistency requirements, then propose a clear separation: OLTP for transactional writes and real-time reads, OLAP for analytical queries and aggregations. Justify the split by discussing trade-offs in latency, scalability, and data freshness, and explain how data flows from OLTP to OLAP (e.g., via CDC or batch ETL).
Pro tip: Emphasize that separation is not just about technology but about aligning with business needs—e.g., Netflix's personalization and analytics teams have different SLAs. Mention that you'd monitor and iterate on the split as access patterns evolve.
Analyze the system's operations: transactional writes (e.g., user actions, billing) vs. analytical reads (e.g., aggregations, reporting). Note latency, throughput, and consistency needs for each.
Propose which components handle OLTP (e.g., a relational DB like PostgreSQL for ACID transactions) and which handle OLAP (e.g., a columnar store like Redshift or Snowflake for complex queries).
Explain how data moves from OLTP to OLAP: batch ETL, change data capture (CDC), or streaming (e.g., Kafka). Discuss trade-offs in latency and complexity.
Discuss how to handle eventual consistency in OLAP and ensure that analytical results are acceptable for the use case (e.g., near-real-time vs. daily).
Summarize why this separation benefits scalability, performance isolation, and cost. Relate to Netflix's scale (e.g., millions of users, global distribution) and mention monitoring/evolution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.