This was basically the whole interview compressed into one prompt.
Start by clarifying requirements and scale, then propose a unified architecture that handles both batch and streaming with a lambda or kappa design. Walk through each layer—ingestion, storage, processing, and serving—highlighting trade-offs and how you'd ensure consistency and low latency.
Pro tip: Emphasize the importance of a unified data model and metadata management to avoid divergence between batch and streaming paths. Also, discuss how you'd handle late-arriving data and exactly-once semantics in streaming.
Ask about data volume, velocity, variety, latency requirements, and consistency needs. Understand what product analytics entails (e.g., user behavior, funnel analysis) and expected query patterns.
Propose a layered architecture: ingestion (e.g., Kafka for streaming, batch ingestion for daily loads), storage (e.g., data lake for raw, data warehouse for processed), processing (e.g., Spark for batch, Flink for streaming), and serving (e.g., OLAP database, cache).
Detail the ingestion layer: how to handle both real-time and batch sources, schema management, and data quality. Discuss storage choices: Parquet for batch, Delta Lake for ACID, and how to unify batch and streaming storage.
Explain batch processing (e.g., Spark) and stream processing (e.g., Flink) with windowing, watermarks, and exactly-once semantics. Address how to reconcile batch and streaming results (lambda vs kappa) and handle late data.
Describe serving options: pre-aggregated tables for batch, real-time dashboards from streaming, and a unified query layer (e.g., Presto). Discuss trade-offs: latency vs throughput, cost, complexity, and consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Spent too long on deduplication and didn't get to late event handling until they prompted me.
Start by clarifying the pipeline's requirements (exactly-once vs at-least-once, latency vs correctness) and then systematically address each concern: idempotency via deterministic keys and upserts, deduplication with stateful stores and TTLs, and late events with watermarks and allowed lateness. Emphasize trade-offs and how you'd validate the solution with metrics and testing.
Pro tip: Meta values practical, scalable solutions: mention how you'd leverage Flink's built-in exactly-once semantics and state management, but also discuss the cost of state and how you'd tune it. Show you understand that perfect exactly-once is often a trade-off with latency and complexity.
Ask about data volume, latency tolerance, correctness guarantees (exactly-once vs at-least-once), and downstream systems. This shapes the entire design.
Use deterministic event IDs and idempotent writes (e.g., upserts with versioning) so retries or duplicates don't corrupt state. Consider idempotent sinks like databases with unique constraints.
Maintain a stateful dedup store (e.g., RocksDB in Flink) with TTL to track seen event IDs. Discuss trade-offs between memory, storage, and accuracy.
Use event-time processing with watermarks and allowed lateness. For events beyond allowed lateness, route to a side output or dead-letter queue for later reconciliation.
Define metrics for duplicates, late events, and state size. Test with fault injection and replay scenarios to ensure correctness under failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the workload characteristics (query patterns, data volume, update frequency) and then explain how you choose partitioning and clustering keys to optimize for the most common queries. Emphasize trade-offs between performance, storage, and maintenance, and give a concrete example from your experience.
Pro tip: Mention that you validate your design with real query patterns and monitor performance metrics, and be prepared to discuss how you handle schema evolution and backfilling. This shows you think about long-term operability, not just initial setup.
Ask about data volume, query patterns (filtering, aggregation, joins), latency SLAs, and update frequency to understand what partitioning and clustering should optimize for.
Select a partition key (e.g., date, region) that aligns with common filters and enables partition pruning; discuss granularity and trade-offs like too many small partitions.
Pick clustering columns (e.g., user_id, event_type) to co-locate related data and speed up range scans and aggregations; explain how clustering complements partitioning.
Test with representative queries, measure performance, and adjust keys as needed; mention monitoring and handling skew or hot partitions.
Cover maintenance tasks like compaction, backfilling, and schema evolution, and how they impact the chosen design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
SCD2 vs SCD1 I know cold, but I fumbled the connection to append-only vs upsert in a streaming context.
Start by contrasting append-only and upsert/merge patterns in terms of data integrity, performance, and use cases. Then explain how slowly changing dimensions (SCDs) fit into these patterns, focusing on Types 1, 2, and 3, and when to use each. Emphasize trade-offs and give examples from your experience.
Pro tip: Mention that append-only is often used for immutable event logs (e.g., Kafka) while upsert/merge is for mutable state (e.g., user profiles). For SCDs, highlight that Type 2 is common for historical tracking but requires more storage and complex queries.
Briefly explain append-only (immutable, event sourcing) and upsert/merge (mutable, current state) patterns, including their pros and cons.
Discuss scenarios: append-only for audit trails, event streaming, and high write throughput; upsert/merge for maintaining current state, handling late-arriving data, and reducing storage.
Explain that slowly changing dimensions manage changes in dimension attributes over time, and describe Types 1 (overwrite), 2 (add new row), and 3 (add new column).
Map SCD types to patterns: Type 1 often uses upsert; Type 2 uses append-only with effective dates; Type 3 uses upsert with additional columns.
Summarize trade-offs (storage, query complexity, performance) and provide a concrete example, such as user profile changes in a social network.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the pipeline's architecture, data volume, and downstream dependencies to frame the backfill challenge. Then walk through a structured approach: assess impact, design an idempotent and isolated backfill process, execute incrementally with monitoring, and validate results. Emphasize trade-offs between correctness, performance, and operational risk.
Pro tip: Always design backfills to be idempotent and run them in a separate environment or with resource isolation to avoid impacting live traffic. Proactively communicate with downstream consumers about the backfill timeline and expected data changes.
Understand why the backfill is needed, what data range is affected, and which downstream systems or SLAs might be impacted. Identify any compliance or data retention constraints.
Ensure the backfill logic is idempotent so it can be safely retried. Isolate the backfill from production traffic by using separate resources, rate limiting, or running during low-traffic windows.
Break the backfill into smaller chunks (e.g., by time partitions) to limit blast radius and allow for progress tracking. Implement checkpointing to resume from failures without reprocessing everything.
Set up monitoring for resource usage, error rates, and data quality. Validate the backfilled data against source systems or expected outputs, and compare with existing data to ensure consistency.
Notify stakeholders about the backfill schedule and potential data changes. Have a rollback plan in case of issues, such as reverting to a previous snapshot or disabling the backfill.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This felt like the most conversational part.
Start by clarifying the pipeline's requirements and constraints, then propose a high-level architecture that separates orchestration, dependency management, and failure recovery concerns. Dive into specific mechanisms for each, emphasizing trade-offs and how you'd ensure reliability and scalability.
Pro tip: Demonstrate maturity by discussing how you'd balance simplicity and robustness—e.g., using managed services vs. custom solutions—and by highlighting observability and idempotency as foundational to failure recovery.
Ask about pipeline complexity, SLAs, data volume, latency requirements, and existing infrastructure to tailor your answer.
Choose an orchestrator (e.g., Airflow, Argo, Step Functions) and explain how it schedules, triggers, and monitors tasks, considering scalability and fault tolerance.
Define dependencies as a DAG, handle data and control dependencies, and discuss strategies for dynamic dependencies and versioning.
Describe retry policies, idempotent tasks, checkpointing, dead-letter queues, and alerting; explain how to recover from partial failures and ensure exactly-once semantics if needed.
Compare approaches (e.g., centralized vs. decentralized orchestration) and explain how the design can evolve with scale and changing requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about pre-aggregating at ingestion time for fixed windows and using approximate methods like HyperLogLog for cardinality in rolling windows.
Start by clarifying the scale, latency requirements, and metric definitions, then propose a layered architecture that separates batch and stream processing. Emphasize trade-offs between accuracy, cost, and latency, and how you would handle late data and exactly-once semantics.
Pro tip: At Meta, metrics often need to be sliced by dimensions like user, region, and device; pre-aggregating with a lambda architecture or using a system like Druid or Pinot can drastically reduce query latency. Always discuss how you'd handle backfills and data reprocessing without disrupting live dashboards.
Ask about data volume, velocity, latency SLAs, accuracy needs, and whether metrics are for real-time monitoring or historical analysis. Understand the dimensions and granularity required.
Decide between batch, stream, or hybrid (lambda/kappa) based on latency and accuracy. For daily metrics, batch may suffice; for hourly and rolling windows, streaming with windowing is often needed.
Use a time-series or OLAP store (e.g., Druid, Pinot, ClickHouse) and pre-aggregate at the finest granularity needed. Consider materialized views or rollups to speed up queries.
Implement watermarks, allowed lateness, and idempotent writes to ensure correctness. Discuss exactly-once semantics and how to reconcile batch and stream results.
Partition and shard data appropriately, use columnar storage, and compress. Monitor and tune for cost-efficiency, and consider tiered storage for older data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about row count checks, null rate thresholds, schema drift detection, and freshness monitoring.
Start by framing data quality as a multi-layered system: preventive checks at ingestion, continuous monitoring in pipelines, and automated alerting. Then define SLAs as contracts with measurable metrics (freshness, completeness, accuracy) and describe enforcement via monitoring, alerting, and escalation. Emphasize how you balance rigor with scalability in a large-scale environment like Meta.
Pro tip: Tie data quality directly to business impact—e.g., 'a 1% drop in data completeness can skew ad targeting metrics by X%'—to show you understand the downstream consequences and can prioritize checks accordingly.
Identify key dimensions like accuracy, completeness, consistency, timeliness, and validity. For each, define measurable metrics (e.g., % nulls, latency, schema drift) that align with business needs.
Apply checks at ingestion (schema validation, format checks), transformation (null checks, referential integrity), and serving (freshness, row counts). Use a mix of batch and streaming validations.
Specify SLAs as contracts: e.g., 'data freshness < 15 min, 99.9% completeness'. Assign owners (data producers/consumers) and document consequences for violations.
Set up automated monitoring with thresholds, anomaly detection, and alerting (e.g., PagerDuty). Integrate with CI/CD to block bad data from propagating.
Track SLA violations, conduct root-cause analysis, and refine checks. Use feedback loops to adjust thresholds and add new checks as data evolves.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.