Advanced Caching and Data Pipeline Design Questions
Distributed caching, cache coherency, specialized stores (search engines, column stores, time-series databases). Data pipeline architecture: batch processing, stream processing, ETL design. Understanding Lambda and Kappa architectures.
HardTechnical
0 practiced
Design a data governance approach for a large analytics platform focusing on data lineage, schema governance, access controls, and ensuring cached materialized views respect row-level security. Include practical tools and policies you'd introduce for enforcement and auditability.
MediumSystem Design
0 practiced
Design a Lambda architecture for a user analytics pipeline: requirements include ingesting 100k events/sec, supporting both near-real-time dashboarding (latency < 5s) and accurate daily aggregates for ad-hoc queries. Explain components for batch and speed layers, materialized views, data storage choices, and how you reconcile results between layers.
HardTechnical
0 practiced
Discuss cache coherence models for distributed caches: invalidation-based, update-based, lease-based, and version-vector approaches. For each model, analyze scaling behavior, network overhead, stale-read windows, and suitability for read-heavy vs write-heavy systems.
EasyTechnical
0 practiced
Implement an LRU (Least Recently Used) cache in Python that supports get(key) and put(key, value) in O(1) time. The cache should be initialized with a fixed capacity, evict the least-recently-used item when full, and get should return -1 for missing keys. Provide a clear single-process implementation and briefly explain how you'd adapt it for concurrent access and TTL support. Example usage: cache = LRUCache(2); cache.put(1,1); cache.get(1) -> 1; cache.put(2,2); cache.put(3,3) evicts key 1.
HardSystem Design
0 practiced
Architect a streaming analytics platform that must process 1M events/sec with end-to-end processing latency under 200ms for most pipelines and exactly-once semantics for financial metrics. Explain choices for message bus, stream processor, state backend, checkpointing, partitioning, and how to ensure exactly-once sinks.
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