Data Architecture and Pipelines Questions
Designing data storage, integration, and processing architectures. Topics include relational and NoSQL database design, indexing and query optimization, replication and sharding strategies, data warehousing and dimensional modeling, ETL and ELT patterns, batch and streaming ingestion, processing frameworks, feature stores, archival and retention strategies, and trade offs for scale and latency in large data systems.
MediumTechnical
0 practiced
You need to migrate on-prem ETL jobs to the cloud with minimal downtime and data loss. Outline a migration plan covering discovery, dual-write or dual-read phases, data validation checks, backfills, canary runs, cutover strategy, rollback criteria, and stakeholder communication. Highlight risk mitigation for each phase.
HardTechnical
0 practiced
Design a data mesh architecture for a large enterprise migrating from a centralized data warehouse. Describe domain data products, federated governance, self-serve platform capabilities (catalog, lineage, discovery), data contracts, interoperability patterns, and a migration roadmap to shift ownership and reduce central bottlenecks.
EasyTechnical
0 practiced
What is a feature store in ML pipelines? Describe the roles of online and offline feature stores, consistency and freshness guarantees required for training vs serving, typical storage/serving technologies, and how a solutions architect would integrate a feature store into an organization's ML platform.
HardSystem Design
0 practiced
For a globally distributed user profile database that must serve low-latency reads and maintain 99.999% availability, design the replication and consistency strategy. Compare leader-follower vs leaderless (gossip/quorum) approaches, discuss conflict resolution strategies, read-after-write guarantees, multi-region latency trade-offs, and how to satisfy high availability and performance simultaneously.
MediumTechnical
0 practiced
Design a sharding and partitioning strategy for a user profile service with 500M users. Choose an appropriate shard key, explain how you'd mitigate hotspots (e.g., celebrity users), outline a re-sharding approach that minimizes downtime, and describe how you'd support cross-shard queries and joins for analytics.
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