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.
HardSystem Design
0 practiced
Design a secure data access model for analytics and model development that enforces row-level and column-level security, supports ephemeral credentials for interactive notebooks, integrates with centralized IAM and a data catalog, and provides auditing for compliance. Discuss performance trade-offs and developer ergonomics.
MediumTechnical
0 practiced
Describe how you would implement monitoring for data pipelines. Provide a runnable checklist of metrics and checks for both batch and streaming pipelines (e.g., lag, throughput, error rates, schema drift), alerting thresholds, and self-healing or escalation actions you would automate for common failures.
MediumTechnical
0 practiced
Discuss the trade-offs of adopting a lakehouse architecture (e.g., Delta Lake) versus maintaining separate data lake and data warehouse systems for a mid-sized analytics and ML platform. Cover ACID guarantees, schema enforcement, query performance, cost model, and engineering operational complexity.
HardTechnical
0 practiced
You must implement exactly-once semantics for a streaming aggregation pipeline that computes features and writes to an online store. Describe how you would achieve exactly-once with Apache Flink or Kafka Streams, and detail strategies for sinks that are not idempotent (e.g., external databases).
EasyTechnical
0 practiced
Compare Parquet, ORC, and Avro as storage formats for large-scale analytics and ML feature tables. As a data scientist choosing a storage format for feature engineering and model training, discuss pros and cons in terms of columnar vs row layout, compression, predicate pushdown/column pruning, schema evolution, and typical read/write performance characteristics.
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