Data Processing and Transformation Questions

Focuses on algorithmic and engineering approaches to transform and clean data at scale. Includes deduplication strategies, parsing and normalizing unstructured or semi structured data, handling missing or inconsistent values, incremental and chunked processing for large datasets, batch versus streaming trade offs, state management, efficient memory and compute usage, idempotency and error handling, and techniques for scaling and parallelizing transformation pipelines. Interviewers may assess problem solving, choice of algorithms and data structures, and pragmatic design for reliability and performance.

EasyTechnical

0 practiced

Define idempotency in the context of data ingestion and transformation. Give two concrete examples of idempotent and non-idempotent operations in a pipeline, and describe practical techniques to make sinks idempotent when producers may retry.

EasyTechnical

0 practiced

List common strategies to handle missing or inconsistent values during transformation for analytics. For each strategy (imputation, deletion, sentinel values, forward/backward fill) explain when it is appropriate and pitfalls that could lead to biased analytics.

HardSystem Design

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Architect a streaming transformation pipeline able to handle 1 million events per second with at-least-once ingestion semantics and an exactly-once sink guarantee. Describe system components, partitioning strategy, state management, checkpointing, how to scale, and how to handle producer and consumer failures.

HardTechnical

0 practiced

Design a compact fingerprinting algorithm to detect near-duplicate textual records across millions of rows where duplicates may have typos and punctuation differences. Discuss approaches such as n-grams, simhash, minhash, locality sensitive hashing, and edit distance approximations and how to scale dedupe to distributed processing.

MediumTechnical

0 practiced

Write a SQL query that flags outlier transactions per user where amount > mean + 3*stddev over that user's past 365 days. Given table transactions(transaction_id, user_id, amount numeric, occurred_at timestamp), assume sufficient historical data and prefer window functions. Explain performance considerations for large tables.

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