This was the main question and it basically ate the whole session.
Start by clarifying requirements and constraints (volume, latency, data sources, analytics needs) before diving into architecture. Then present a layered pipeline: ingestion, processing, storage, and serving, explaining trade-offs at each stage and how they align with ML use cases at Expedia.
Pro tip: Emphasize data quality and schema evolution from the start—interviewers at Expedia value reliability and scalability for travel data, which is seasonal and high-volume. Also, mention how your design supports ML feature engineering and model training, not just analytics.
Ask about data volume, velocity, variety, latency requirements, and downstream consumers (analytics, ML models). Identify non-functional requirements like fault tolerance, scalability, and cost.
Choose between batch and stream ingestion based on requirements. Propose using a distributed message queue (e.g., Kafka) to handle multiple producers, with schema registry for data contracts and exactly-once semantics.
Outline stream processing (e.g., Flink, Spark Streaming) for real-time transformations and batch processing (e.g., Spark) for historical data. Include data cleaning, enrichment, aggregation, and feature computation for ML.
Propose a data lake (e.g., S3) for raw and processed data, and a data warehouse (e.g., Snowflake, BigQuery) for analytics-ready storage. Consider a feature store for ML features and a serving layer for low-latency access.
Discuss fault tolerance, backpressure, data quality checks, lineage, and monitoring. Explain how to handle schema evolution, late data, and reprocessing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the tension between schema evolution and downstream stability, then propose a strategy that combines backward/forward compatible schema changes with a schema registry and consumer-driven contracts. Emphasize incremental rollout, monitoring, and fallback mechanisms to prevent breakage in high-throughput ML pipelines.
Pro tip: Highlight the importance of treating schemas as APIs with versioning and deprecation policies, and mention that in ML pipelines, feature stores often act as a buffer to decouple producers and consumers, allowing safe schema evolution.
Identify all downstream consumers (models, dashboards, etc.) and their schema expectations. Document which fields are critical and how they are used.
Use a schema registry (e.g., Confluent Schema Registry) to enforce compatibility levels (backward, forward, full) and automate validation of schema changes.
Prefer additive changes (new optional fields) and avoid breaking changes like renaming or removing fields. Use default values and versioned schemas to support mixed-version consumers.
Deploy schema changes incrementally, monitor consumer health and data quality metrics, and be ready to roll back if issues arise.
Define a schema evolution policy, communicate changes to stakeholders, and provide migration guides for consumers to adapt over time.
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 requirements (latency, throughput, consistency, cost) and then compare stream processing and micro-batch on those dimensions. Use a concrete example from Expedia's domain (e.g., real-time pricing or fraud detection) to ground the tradeoffs and show how you'd choose based on business needs.
Pro tip: Acknowledge that many 'real-time' ML use cases can tolerate a few seconds of latency, making micro-batch a simpler and more cost-effective choice; reserve true stream processing for when sub-second latency or complex event-time logic is essential.
Ask about latency SLAs, data volume, consistency needs, and operational constraints to frame the comparison.
Briefly explain stream processing (event-at-a-time, low latency) vs. micro-batch (small batches, higher latency but simpler).
Discuss latency, throughput, fault tolerance, state management, and exactly-once semantics for each approach.
Map tradeoffs to ML-specific concerns like feature freshness, model retraining frequency, and online inference.
Propose a choice based on the clarified requirements, and mention hybrid or lambda architectures if appropriate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the complexity of backfilling a live production pipeline, then outline a strategy that prioritizes data consistency and minimal disruption. Emphasize idempotency, versioning, and a phased rollout with thorough validation.
Pro tip: Design backfills as first-class citizens from day one by making pipelines idempotent and versioned, so future backfills are just a matter of replaying with a new version. This shows foresight and reduces operational risk.
Identify which data needs backfilling, the time range, and the impact on downstream consumers. Determine if the backfill can be done in-place or requires a separate pipeline.
Ensure the pipeline is idempotent so re-running produces the same results. Use versioned datasets or tables to isolate backfill data from production data.
Run the backfill in a separate environment or with resource isolation to avoid impacting production. Use parallel processing to speed up the backfill while monitoring resource usage.
Compare backfilled data against expected results or a golden dataset. Check for consistency, completeness, and correctness before promoting to production.
Gradually switch over to the backfilled data, monitoring for anomalies. Have a rollback plan in case of issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a layered approach: validation at ingestion, quarantine to a dead-letter queue, alerting on error rates.
Start by framing data quality as a multi-layered pipeline concern: validation at ingestion, monitoring in flight, and remediation post-hoc. Then walk through a concrete architecture that handles malformed events gracefully at scale, emphasizing trade-offs between strictness and availability. Finally, tie it back to ML-specific needs like feature freshness and training-serving consistency.
Pro tip: Show you understand that not all bad data is equal—distinguish between recoverable (e.g., schema drift) and unrecoverable (e.g., corrupt payloads) errors, and design your system to quarantine rather than drop, enabling later reprocessing. This demonstrates production maturity and cost-awareness.
Establish explicit schemas, SLAs, and quality dimensions (completeness, accuracy, timeliness) for each event stream. Use schema registry and enforce validation at the edge (e.g., producers) and at ingestion.
Apply lightweight syntactic checks (JSON parse, required fields) first, then semantic checks (range, referential integrity). Route failures to a dead-letter queue or quarantine store with metadata for debugging and reprocessing.
Track metrics like validation failure rate, schema drift, and latency; set up automated alerts and dashboards. Use statistical process control to detect anomalies in event distributions.
Build tooling to fix common issues (e.g., schema evolution, default values) and replay quarantined events after correction. Ensure idempotency and exactly-once semantics where needed.
Validate feature distributions, detect training-serving skew, and monitor model performance degradation due to data quality. Feed insights back to upstream producers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about end-to-end latency tracking, data freshness metrics, and column-level lineage for debugging.
Start by clarifying the pipeline's components and business context, then outline a layered observability strategy covering data, model, and system health. Define SLAs that are meaningful to stakeholders by linking them to user experience and business metrics, and explain how lineage enables root cause analysis and trust.
Pro tip: Tie SLAs to business outcomes (e.g., booking conversion impact) rather than just technical metrics, and mention that you'd implement automated lineage tracking to reduce manual debugging overhead.
Ask questions to understand the pipeline's data sources, processing steps, model deployment, and business goals. Identify key stakeholders and their expectations for reliability and performance.
Propose monitoring at three levels: data quality (freshness, schema, drift), model performance (accuracy, latency, bias), and system health (resource usage, errors). Suggest tools like Prometheus, Grafana, and custom dashboards.
Describe how to capture lineage from data ingestion to model predictions, using metadata stores (e.g., MLflow, Amundsen) and logging. Explain how lineage aids debugging, impact analysis, and compliance.
Define SLAs that align with business impact, such as prediction latency <100ms for 95% of requests, model accuracy within 2% of baseline, and data freshness <1 hour. Include error budgets and escalation paths.
Explain how you'd use observability data to continuously improve the pipeline, set up alerts, and conduct post-mortems. Emphasize collaboration with product and engineering teams to refine SLAs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current scale and cost drivers, then propose a multi-layered strategy that includes architectural changes, algorithmic optimizations, and cost monitoring. Emphasize trade-offs between cost, latency, and accuracy, and how you would prioritize based on business impact.
Pro tip: Quantify the impact: estimate the cost per event or per prediction at current scale, then project the savings from each optimization. This shows you think in terms of ROI and can justify engineering investments.
Ask about current event volume, cost breakdown (compute, storage, network), and SLAs. Establish a baseline to measure improvements.
Analyze where costs scale linearly or worse with volume. Common drivers: model inference, data processing, storage, and I/O.
Suggest techniques like model quantization, caching, batch processing, serverless, and distributed systems. Prioritize based on impact and effort.
Set up cost and performance monitoring, and use auto-scaling to match resources to demand. Consider spot instances and reserved capacity for cost savings.
Continuously measure cost per event and model performance. Be prepared to trade off accuracy for cost if business allows, and validate with A/B tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.