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.
EasyTechnical
46 practiced
Explain data lineage: what it is, why it matters for debugging, compliance, and impact analysis, and how you would capture end-to-end lineage across batch and streaming pipelines. Describe tooling or metadata patterns you would recommend to make lineage usable for engineers and auditors.
MediumTechnical
43 practiced
A regulated fintech needs to choose between a data lake and a data warehouse for analytics. Compare suitability for auditability, schema enforcement, query performance, cost, and security. Propose a hybrid (lakehouse) architecture addressing compliance, lineage, and controlled self-serve analytics for business users.
EasyTechnical
58 practiced
Compare relational databases to NoSQL stores (document, key-value, wide-column, graph) across schema flexibility, consistency, query expressiveness, indexing, and scaling. For a product catalog with nested attributes and heavy reads, explain when a document store is preferable to a relational DB and what hybrid options you might propose.
MediumSystem Design
45 practiced
Design a comprehensive monitoring and alerting strategy for data pipelines. List key metrics (throughput, lag, completeness, error-rate, data-skew), describe alert thresholds and escalation policies, and outline automated remediation steps (retries, reprocessing) and runbook examples for common failure modes.
MediumSystem Design
60 practiced
Design an online + offline feature store architecture: specify storage technologies for low-latency serving vs batch offline features, consistent computed feature pipelines, materialization frequency, read APIs, caching strategies, and validation to ensure offline training features match online serving features.
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