Pipelines, batch + stream (Spark, Kafka), warehouses (Snowflake, BigQuery), schema design, data quality.
Primary categories: data-engineering · backend
Amazon's signature L5+ system design. Tests consistent hashing, replication, quorum, vector clocks, and the CAP trade-offs Amazon engineers grade for keenly. Based on the 2007 Dynamo paper.
Relational + NoSQL hybrid, OLTP vs OLAP separation, global tables. Tests data-modeling decisions at multi-region scale.
Vehicle → edge → cloud, sensor data compression, Kafka/Pulsar, hot/cold tiering, retraining loop.
Google L4+ favorite. Tests append-only event ingestion, time-partitioned storage, materialized views for "Continue Watching," and read-time aggregations under privacy constraints.
Master + chunkservers, replication, append-only writes, fault recovery. Foundation of modern data infra.
Partitioning, ISR, leader election, consumer groups. Meta E6+ infra signature.
Vehicle → edge → cloud, millions of vehicles, sensor data compression, hot/cold tiering, anomaly detection. Tesla #1 SDI.
Structured story format used by every Mag7 behavioral round. Google extends to STAR-L (Learnings). Amazon expects 1 LP per story. Netflix tunes to culture pillars.