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
Describe how to implement CDC-based incremental ingestion into a data warehouse while ensuring correct ordering and idempotency. Include use of transaction IDs or LSNs, watermarking, deduplication strategies, replay handling for connectors (Debezium/Kafka Connect), and approaches for schema changes and backfills.
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.
MediumSystem Design
0 practiced
Design a streaming ingestion architecture capable of 200k events/second to support both sub-second real-time analytics and durable long-term storage. Specify ingestion layer (Kafka/Kinesis), buffering/backpressure handling, processing framework (Flink/Beam), state management, windowing semantics, checkpointing for exactly-once, and how to route events to OLAP and archival tiers.
MediumTechnical
0 practiced
Discuss trade-offs between ETL and ELT when migrating on-prem pipelines to cloud data warehouses (Snowflake/BigQuery): include compute billing, data movement costs, transformation locality, governance/fine-grained control, and security implications. Outline a migration phasing plan that minimizes disruption and cost spikes.
EasyTechnical
0 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.
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