Data Consistency and Idempotency Questions
Understand how to maintain correct data in distributed and asynchronous systems and how to design idempotent operations so retries do not produce duplicate effects. Cover the relationship between consistency models and idempotency, transactional guarantees across components, patterns for idempotent request handling, unique request identifiers, deduplication, compensating transactions, and when to use eventual reconciliation or strong transactional boundaries. Discuss how idempotency affects API design, retry strategies, and user visible correctness.
EasyTechnical
0 practiced
How should API versioning and schema evolution be handled in systems where idempotency keys are part of request payloads? Discuss backward compatibility, migration of idempotency key semantics, and coordination patterns between clients and servers to avoid accidental duplicates during upgrades.
MediumTechnical
0 practiced
Design a monitoring and alerting approach to detect duplicates or inconsistency in real-time data pipelines. List the key metrics you would collect (e.g., duplicate rate, replays, offset lags), explain thresholds/SLAs, and provide a remediation playbook for on-call engineers including automated and manual steps.
EasyTechnical
0 practiced
Compare and contrast strong (linearizability/serializability) and eventual consistency models in distributed systems. For each model, provide one data engineering example (e.g., profile updates vs. analytics ingestion) where it is appropriate and explain the trade-offs regarding latency, availability, and how idempotency interacts with each model.
MediumTechnical
0 practiced
Explain how you would implement 'exactly-once' semantics using Kafka and Spark Structured Streaming. Discuss transactional producers, idempotent producers, checkpointing, sink semantics, and the role of write-ahead/outbox patterns. Describe failure modes that still lead to duplicates and mitigations.
EasyTechnical
0 practiced
List and briefly describe common deduplication approaches used by data engineers: producer-side dedupe, consumer-side dedupe using state, dedupe tables/upserts, watermark/window compaction. For each approach give a short note about one key advantage and one drawback.
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