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
Write a SQL query (standard ANSI SQL) to compute Monthly Active Users (MAU) and 1-day retention rate given an events table.Schema:Return a result with columns: month (YYYY-MM), mau, next_day_retention_pct (percentage of users active next calendar day).
events(event_id STRING, user_id STRING, event_time TIMESTAMP, event_type STRING)EasyTechnical
0 practiced
Explain schema-on-read vs schema-on-write and the implications for BI teams when source schemas evolve. Include pros/cons for validation, speed, and flexibility.
HardSystem Design
0 practiced
Design a feature store or shared derived-tables layer that supports BI features used in dashboards and ML feature consumption. Cover offline and online stores, freshness guarantees, serving APIs, lineage, and how to version features so BI reports remain reproducible.
HardTechnical
0 practiced
Design a cost-optimized storage strategy for 5 years of clickstream data where daily dashboards require fast access to the last 30 days and occasional ad-hoc queries target older history. Describe hot/cold tiers, pre-aggregations, compression/file formats, and query patterns for archival retrieval.
MediumTechnical
0 practiced
Design a data archival and retention policy for an analytics platform that must keep 2 years of detailed events online, archive years 3–5 for occasional queries, and fully delete data older than 5 years to comply with regulations. Describe storage tiers, access patterns, and process for restoring archived data to serve ad-hoc BI requests.
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