← Databricks Interview Insights
This is the kind of question that feels straightforward until you start drawing boxes and realize you haven't thought about what happens when a document fails halfway through.
Start by clarifying requirements and scale, then propose a high-level architecture that leverages Databricks' Lakehouse platform (Delta Lake, Spark) for ingestion, processing, and serving. Focus on trade-offs between batch and streaming, and discuss how to ensure reliability, scalability, and cost-efficiency.
Pro tip: Emphasize how Delta Lake's ACID transactions and time travel simplify pipeline reliability and reproducibility, and mention how you'd use Databricks Workflows for orchestration and monitoring.
Ask about data volume, velocity, variety, latency requirements, and use cases (e.g., analytics, ML). Confirm SLAs and budget constraints.
Outline stages: ingestion (batch/streaming), storage (Delta Lake), processing (Spark), and serving (BI, ML). Mention Databricks components like Auto Loader, Structured Streaming, and MLflow.
Discuss ingestion patterns (e.g., Auto Loader for incremental files), processing (batch vs. streaming, medallion architecture), and storage optimizations (partitioning, Z-ordering).
Compare batch vs. streaming, cost vs. latency, and how to scale (e.g., autoscaling clusters, Delta Engine). Discuss failure handling and exactly-once semantics.
Explain how to monitor pipeline health (Databricks Jobs UI, metrics), ensure data quality (expectations, Delta constraints), and implement governance (Unity Catalog).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.