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
72 practiced
A Spark job that joins three very large DataFrames (hundreds of millions of rows) runs slowly and frequently fails with 'ExecutorLost' and OOM errors. Given limited cluster resources, outline a step-by-step troubleshooting and optimization plan: what metrics and UI pages to inspect, code rewrites or refactors you'd try, caching strategies, config knobs to tune, and potential architectural changes.
HardTechnical
57 practiced
For large distributed query engines (Spark/Presto) where broadcast joins are not possible, describe advanced techniques to optimize joins: repartitioning strategies, pre-aggregation, bloom filters, join reordering, memory and spill tuning, and query planner hints. Provide concrete configuration knobs and explain when to apply each technique.
MediumSystem Design
87 practiced
Design the schema and indexing strategy for an analytical table that stores user events partitioned by day and queried frequently for arbitrary date ranges and user segments. Include recommendation on partitioning scheme, clustering/sort keys, secondary indexes or materialized views, and how to optimize for both scan-heavy analytics and selective point queries.
HardTechnical
47 practiced
At petabyte scale with many small files written by many producers, reads are suffering due to metadata overhead and small-file penalties. Design a partitioning and compaction strategy: choose file format, target file sizes, compaction scheduling (real-time vs periodic), incremental compaction approaches, and how to make compaction non-disruptive to producers and consumers.
MediumTechnical
47 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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