BigData / Data Lake Interview questions
What monitoring and observability practices should be implemented for Data Lakes?
Monitoring and observability provide visibility into data lake health, performance, and usage. Comprehensive monitoring prevents issues, enables quick troubleshooting, and optimizes operations.
Monitoring Dimensions:
1. Infrastructure Metrics: Storage usage and growth, compute utilization (CPU, memory), network throughput and latency, API request rates and errors, cluster health.
2. Data Pipeline Metrics: Job success/failure rates, processing duration and SLA compliance, data volume processed, backlog and lag for streaming pipelines, error rates and retry counts.
3. Data Quality Metrics: Schema validation failures, null/missing value percentages, record count anomalies, data freshness (time since last update), quality score trends.
4. Query Performance: Query execution time, data scanned per query, query failure rates, concurrent queries, cost per query.
5. Access and Security: Failed authentication attempts, unauthorized access attempts, permission changes, sensitive data access patterns, unusual access times/locations.
6. Cost Metrics: Storage costs by tier and project, compute costs by workload, data transfer costs, cost trends and anomalies.
Monitoring Tools:
- Cloud Native: AWS CloudWatch, Azure Monitor, Google Cloud Monitoring
- Open Source: Prometheus + Grafana, ELK Stack (Elasticsearch, Logstash, Kibana)
- Commercial: Datadog, New Relic, Splunk, Monte Carlo (data observability)
- Data-Specific: Datafold, Soda, Bigeye for data quality monitoring
Alerting Strategy: Define SLAs for critical pipelines, set thresholds for alerts (warning vs critical), avoid alert fatigue with proper tuning, route alerts to responsible teams, implement escalation for critical issues, use PagerDuty/Opsgenie for on-call rotation.
Logging: Centralize logs from all components, structured logging for easier parsing, retain logs per compliance requirements, enable log analysis for troubleshooting.
Dashboards: Executive dashboard (high-level KPIs), operational dashboard (system health), pipeline-specific dashboards, cost analytics dashboard.
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