I went with the usual suspects: users, content, subscriptions, watch history, maybe a separate table for genres or tags.
Start by clarifying the core entities and business requirements (e.g., users, videos, viewing events) before diving into tables. Then propose a normalized schema with primary and foreign keys, and explain how it supports common queries like recommendations and analytics. Finally, discuss trade-offs between normalization and denormalization for performance.
Pro tip: Emphasize how the schema supports data science workflows—such as feature engineering for recommendations—by including event timestamps and user-video interaction tables. This shows you understand the role beyond just database design.
Ask about the streaming service's scale, key features (e.g., recommendations, user profiles), and data access patterns. This ensures the schema aligns with business needs.
List main entities like Users, Videos, Genres, and ViewingEvents. Define how they relate (e.g., one-to-many, many-to-many) to establish the ER model.
For each entity, specify tables with primary keys, foreign keys, and essential columns (e.g., user_id, video_id, timestamp). Ensure normalization to reduce redundancy.
Discuss indexing, partitioning, and potential denormalization for read-heavy analytics. Mention trade-offs between consistency and latency.
Walk through example queries (e.g., 'top videos by genre') to show how the schema supports them efficiently. Highlight any adjustments needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the table schema and define 'bought together' as products appearing in the same order. Then, self-join the transactions table on order to generate product pairs, ensuring each pair is counted once by enforcing product1 < product2. Finally, aggregate to count occurrences and return the top pair.
Pro tip: Mention that you would exclude orders with only one product and consider handling ties or using a window function like RANK() to get the most frequent pair. Also, discuss performance implications of self-joins on large datasets and possible optimizations.
Confirm the table structure (e.g., columns: user_id, order_id, product_id) and define what 'most frequently bought together' means (e.g., pair of distinct products in the same order).
Self-join the transactions table on order_id to create all possible pairs of products within each order, ensuring each pair is counted once by filtering product1 < product2.
Group by the product pair and count the number of orders in which each pair appears.
Order the results by count descending and limit to 1 (or use a window function to handle ties) to get the most frequent pair.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard, used np.where for a simple binary condition and mentioned that for more branches you'd reach for np.select.
Start by clarifying the conditions and the desired output column, then demonstrate using numpy.select or pandas apply for multiple conditions, and finally discuss performance trade-offs and alternatives like vectorized operations. Emphasize readability, efficiency, and scalability for large datasets.
Pro tip: Mention that for complex conditions, numpy.select is often faster and more readable than apply, but for simple binary conditions, direct boolean indexing is most efficient. Also, note that using pd.cut or pd.qcut can be useful for binning continuous variables.
Ask about the number of conditions, the size of the DataFrame, and whether the conditions are based on single or multiple columns. This ensures you choose the most appropriate method.
For simple if-else, use numpy.where or boolean indexing; for multiple conditions, use numpy.select; for complex row-wise logic, use apply. Discuss the trade-offs in terms of performance and readability.
Write concise code demonstrating the chosen method, ensuring it handles edge cases like missing values. For example, using numpy.select with a list of conditions and choices.
Mention vectorization benefits and how to avoid iterrows. Validate the new column by checking value counts or sample rows to ensure correctness.
If the dataset is large, highlight the importance of vectorized operations and possibly using Dask or PySpark for out-of-core computation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.