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
Design a dimensional model (star schema) for an e-commerce analytics warehouse to support queries such as 'sales by category by week' and 'repeat customer rate'. Specify fact table grain, necessary dimensions (product, customer, time), surrogate keys, handling of slowly changing dimensions (SCD Type 1/2), and how to support ad-hoc attributes.
MediumSystem Design
0 practiced
Design a streaming ingestion pipeline that can handle 100,000 events per second (assume average event size 1KB), persist raw events durably, provide low-latency metrics (sub-second to 5s), support replay from any offset, and offer exactly-once processing guarantees to sinks. Describe components, partitioning strategy, monitoring, and how you would scale and test this pipeline.
HardTechnical
0 practiced
Design a comprehensive testing strategy for data pipelines: include unit tests for transforms, integration tests for DAGs, contract/schema tests, data quality/regression tests, and canary deployments. Provide concrete example test cases, tooling recommendations (e.g., pytest, dbt tests, Great Expectations), and how to automate them in CI/CD.
HardTechnical
0 practiced
Design a robust schema versioning strategy for event schemas exposed by producers and consumed by many downstream services. Discuss Avro/Protobuf/JSON Schema choices, schema registry usage, compatibility modes (backward/forward/full), CI enforcement, and strategies for rolling out breaking changes with minimal consumer disruption.
MediumTechnical
0 practiced
Explain indexing strategies for large analytical workloads: columnar storage benefits, min/max statistics and zone maps, clustering/partition keys, bloom filters, and secondary indexing. For each strategy, state which query patterns they help and describe maintenance costs or write amplification.
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