InterviewCloud
Lakehouse, Spark, Delta Lake, MLflow, feature engineering, and production data/ML pipeline questions.
A lakehouse combines low-cost data lake storage with warehouse-like reliability, governance, and performance.
Mention open storage, ACID tables, schema enforcement, batch and streaming, BI workloads, ML workloads, and unified governance.
Delta Lake adds ACID transactions, schema management, time travel, and reliable streaming/batch tables.
Interviewers want practical benefits: fewer corrupt tables, safer upserts, reproducibility, governance, and performance features like compaction and data skipping.
Check the Spark UI for skew, shuffles, spills, task imbalance, partition count, and expensive joins.
A strong answer includes broadcast joins, repartitioning, caching carefully, predicate pushdown, file sizes, cluster sizing, and avoiding wide transformations where possible.
MLflow tracks experiments, packages models, registers versions, and supports deployment workflows.
Use it to compare runs, store metrics and artifacts, promote models through stages, reproduce training, and integrate model serving with monitoring.
Data Engineer Associate/Professional and Machine Learning credentials can help structure interview preparation.
For interviews, focus less on badges and more on Spark reasoning, Delta Lake design, pipeline reliability, MLflow, and hands-on lakehouse projects.