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.

HardTechnical

35 practiced

Design a checkpoint and recovery algorithm for a custom stateful operator that maintains tens of millions of keys. Discuss how you would snapshot state efficiently, perform incremental state transfers, and restore with minimal downtime and network overhead.

MediumTechnical

31 practiced

You receive dates from multiple sources in formats like '2021-07-01', '07/01/2021', '1 Jul 2021', and epoch milliseconds. Describe a robust algorithm to normalize these to ISO-8601 while handling timezones, ambiguous day/month order, and noisy inputs. Mention libraries you would use and validation heuristics.

MediumTechnical

36 practiced

Describe how to implement external merge sort to sort a file of records larger than available memory. Provide pseudocode and explain the stage of creating sorted runs, merging them, the number of passes, temporary storage needs, and how to tune for disk I/O and memory constraints.

HardSystem Design

42 practiced

Architect a low-latency feature store ingestion system that provides point-in-time correctness for ML training and supports incremental materialization for online features. Describe ingestion semantics, feature serving, online store consistency, and how to guarantee point-in-time joins during offline training.

HardTechnical

36 practiced

Describe a CI/CD strategy for versioning, testing, and deploying data transformation code and schemas so that schema changes do not break downstream analytics. Include unit tests, integration tests with sample data, schema registry checks, canary deployments, and migration rollback plans.

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