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.

EasyTechnical

0 practiced

Describe several simple strategies a data scientist can use to deduplicate records before joining, including deduping by timestamp (keep latest), deterministic tie-breakers, aggregation (sum/mean), and canonicalization. Explain trade-offs and how to preserve provenance so downstream audits can trace which record was kept.

HardSystem Design

0 practiced

A downstream join fails after a source column was renamed and a new nullable field added. Design a robust strategy to handle schema evolution that minimizes pipeline breakage: include schema registries, backward/forward compatibility rules, automated detection, and safe migration patterns for join keys.

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.

MediumTechnical

0 practiced

You run a distributed join and notice one executor is overloaded due to key skew. Describe how you would detect key skew using job metrics and explain at least three mitigation techniques (salting, key bucketing, broadcast small table) and the trade-offs of each in Spark or distributed SQL engines.

MediumTechnical

0 practiced

You have a dataset of customer addresses and need to join to a third-party geocoding reference to get lat/long. Describe an end-to-end approach including address parsing/standardization, external API or reference database selection, blocking or candidate selection to reduce lookups, and how you would validate match quality and propagate uncertainty into downstream models.

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