Started okay, picked an e-commerce transaction as my example and defined grain at the order-line level.
Start by clarifying the business process and analytics requirements, then choose the grain of the fact table before identifying dimensions. Walk through the star schema design, explaining how each dimension supports slicing and dicing of measures.
Pro tip: Emphasize that the grain determines the level of detail and must be declared before choosing dimensions; also mention that surrogate keys and slowly changing dimensions (SCDs) are critical for historical accuracy.
Ask questions to understand the transactional event (e.g., sales, orders, clicks) and the key metrics and dimensions needed for analysis.
State the most atomic level of detail captured, such as one row per transaction line item, and explain why this grain supports flexible aggregation.
List the dimension tables (e.g., Date, Customer, Product, Store, Promotion) and describe their attributes and surrogate keys.
Specify the numeric measures (e.g., quantity, revenue, discount) and note which are additive, semi-additive, or non-additive.
Mention how slowly changing dimensions are handled (Type 1/2/3) and any indexing or partitioning strategies for large fact tables.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining SCD Type 2 and its purpose: preserving full history by creating a new row for each change, with effective dates and a current flag. Then walk through the implementation steps: adding metadata columns, detecting changes, expiring old records, inserting new ones, and handling late-arriving data. Finally, discuss trade-offs like storage and query complexity, and how to optimize for performance.
Pro tip: Emphasize the importance of a surrogate key and a natural/business key combination to uniquely identify each version, and mention that using a current flag (is_current) can simplify queries but requires careful maintenance. Also, consider partitioning by effective date for scalability.
Explain that SCD Type 2 tracks historical changes by creating a new record for each change, preserving full history. Mention that it's ideal for dimensions where historical context matters, like customer address or product category.
Add metadata columns: surrogate key (unique per version), natural/business key (identifies the entity), effective_start_date, effective_end_date, and is_current flag. Optionally include a version number or change reason.
Use a source-to-target mapping to compare incoming records with current active records. For each change, expire the existing row by setting end_date and is_current=false, then insert a new row with new surrogate key and current dates.
Discuss strategies for out-of-order updates, such as using effective dates to insert records in the correct historical position and adjusting end dates of adjacent records. Also mention handling deletes (soft deletes) and nulls.
Acknowledge increased storage and query complexity. Suggest optimizations like partitioning by effective date, indexing on natural key and is_current, and using merge/upsert operations for efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on a clean many-to-many example that wasn't just products and orders.
Start by defining two concrete example tables (e.g., User and Order) to illustrate one-to-many, then introduce a third table (e.g., Product) to show many-to-many via a junction table. Contrast how each cardinality affects schema design, focusing on foreign key placement, junction tables, and query patterns. Conclude with trade-offs in normalization, performance, and scalability.
Pro tip: Mention that many-to-many relationships often require additional attributes on the junction table (e.g., quantity, timestamp), which can turn it into an associative entity—this shows you think beyond textbook examples.
Choose simple, relatable tables like User and Order for one-to-many, and Student and Course for many-to-many. Clearly state the entities and their attributes.
Describe how one row in Table A relates to many rows in Table B, and show that the foreign key is placed on the 'many' side (e.g., Order.user_id).
Describe how many rows in Table A relate to many rows in Table B, requiring a junction table (e.g., Enrollment) with foreign keys to both tables.
Compare how one-to-many uses a simple foreign key, while many-to-many adds a junction table, affecting normalization, indexing, and join complexity.
Mention performance implications (e.g., extra joins), data integrity constraints, and when denormalization might be considered for read-heavy workloads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the conversation got interesting.
Start by clarifying the use case and access patterns, then compare bridge table vs. fact table approaches based on cardinality, query flexibility, and performance. Conclude with a recommendation that balances normalization, query complexity, and scalability for the given scenario.
Pro tip: At Meta, scale and query patterns often drive schema decisions—emphasize how your choice optimizes for read-heavy workloads and avoids expensive joins at scale. Mention that you'd validate with real query plans and data volume estimates before committing.
Ask about the nature of the relationship (e.g., users and groups), expected data volume, and primary queries (read vs. write, aggregation needs). This determines whether a bridge table or fact table is more appropriate.
Describe a bridge table (junction table) with foreign keys to both entities, possibly with additional attributes. Highlight its simplicity, normalization, and flexibility for many-to-many relationships without extra measures.
Describe using a fact table where each row represents an event or transaction linking the two entities, often with measures (e.g., timestamp, count). This is common in data warehousing and supports analytical queries.
Discuss trade-offs: bridge tables are simpler and more normalized but may require joins for analytics; fact tables denormalize and can improve query performance for aggregations but may introduce redundancy and complexity.
Tie back to the initial requirements: for transactional systems, bridge table; for analytical/warehouse systems, fact table. Mention hybrid approaches if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current schema and the specific new requirement, then propose a migration strategy that preserves the single authoritative fact table. Emphasize backward compatibility, scalability, and data integrity throughout the evolution process.
Pro tip: Always consider the impact on downstream consumers and ETL pipelines; propose a phased rollout with dual-write and backfill to minimize disruption.
Ask questions to understand the new requirement, the existing schema, and how the fact table is used. Identify constraints like data volume, query patterns, and SLAs.
Evaluate options: adding a column, creating a new dimension, normalizing, or adding a fact. Consider trade-offs between schema changes and maintaining a single fact table.
Outline steps for schema alteration, data backfill, and dual-write if needed. Ensure the fact table remains authoritative and scalable.
Discuss partitioning, indexing, and denormalization strategies to handle growth. Consider columnar storage and materialized views for performance.
Plan for data validation, testing, and monitoring post-migration. Ensure rollback strategy and communication with stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.