Data Joining and Merging Strategies Questions

Focuses on combining datasets correctly and efficiently. Includes different join types such as inner, left, right, full outer, and cross joins; implications of each join type for result cardinality and missing data; strategies for resolving many to many relationships and duplicate records; methods for identifying and cleaning and aligning join keys including normalization and fuzzy matching; handling mismatched or missing keys and null semantics; performance and memory considerations when joining large tables or distributed datasets; and testing and validation to ensure joins preserve referential integrity and do not introduce inadvertent data leakage.

HardTechnical

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Design an algorithmic approach using locality-sensitive hashing (LSH) to perform approximate string/fuzzy joins on 100M product titles. Explain choice of shingling (character vs token), hash families, candidate generation, memory and disk considerations, how to tune recall vs precision, and how to validate results at scale.

HardTechnical

0 practiced

Compare nested-loop join, hash join, and sort-merge join in terms of algorithmic complexity, memory behavior, and practical DBMS considerations. For each algorithm, describe scenarios (data size, indexing, availability of sorted input) where it is the optimal choice and how you would influence the planner to choose it.

MediumTechnical

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Describe why accurate join cardinality estimates matter for query planners and how a data scientist can help improve them. Include steps such as collecting column statistics, creating histograms, updating stats after large data loads, and the effect of skew and correlated columns on estimates.

MediumTechnical

0 practiced

Outline a scalable approach to perform fuzzy joins between two street-address datasets with 1M records each. Describe steps including normalization, blocking strategy, choice of similarity metric, candidate scoring, and techniques to estimate precision and recall. Mention tools or libraries you would use in Python or Spark and approximate computational complexity.

EasyTechnical

0 practiced

Describe how NULL values in join keys are treated by SQL joins (inner, left, right, full) and by pandas merging. Specifically explain whether NULLs match each other, how that affects record counts after joins, and practical approaches to handle NULLs in join keys before merging datasets for analysis.

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