ADVOPS04-BP01 Implement operational procedures based on data classification and latency requirements
Managing advertising workloads requires different operational approaches based on data latency needs. This best practice focuses on establishing specific procedures for handling low-latency data like bid requests, medium-latency data such as campaign optimization, and high-latency data including historical analytics.
Implementation guidance
For low-latency data (bid data, user profiles, real-time impressions):
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Implement multi-AZ deployments with automatic failover mechanisms to facilitate continuous availability
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Configure monitoring with short evaluation periods appropriate for detecting real-time issues
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Establish dedicated rapid-response procedures for critical alerts affecting bidding operations
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Implement circuit breakers in API calls to help block cascading failures during service degradation
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Create runbooks for emergency traffic management during extreme load conditions
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Configure auto-scaling with aggressive scaling policies to handle sudden traffic spikes
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Implement local caching strategies to reduce database load for frequently accessed data
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Set up dedicated dashboards with high-frequency metric collection for real-time monitoring
For medium-latency data (behavioral data, campaign optimization):
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Configure batch processing jobs with appropriate completion targets for campaign optimization
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Implement queue management with automated retry mechanisms for failed operations
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Set up monitoring with balanced evaluation periods suitable for near real-time operations
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Create standard incident response procedures with appropriate escalation paths
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Implement data validation checks with error handling for data quality issues
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Configure auto-scaling based on processing queue depth and scheduled campaign activities
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Set up dashboards with appropriate refresh rates for campaign management operations
For high-latency data (historical data, analytics):
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Schedule batch processing during off-peak hours to minimize impact on real-time operations
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Implement cost-optimized storage strategies with appropriate data lifecycle policies
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Configure monitoring with periodic health checks and summary reporting
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Create standard support procedures with appropriate response times for non-critical systems
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Implement automated data quality validation with notification mechanisms
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Configure resource allocation with scheduled scaling based on known processing windows
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Establish regular performance review processes with trend analysis
For specialized advertising data types:
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Fraud detection data: Implement optimized processing pipelines with appropriate monitoring and escalation procedures designed for the critical nature of fraud detection
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Content moderation data: Create workflows that balance automated screening with human review processes, with appropriate prioritization based on content risk assessment
Key AWS services
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Amazon CloudWatch
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AWS Systems Manager
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Amazon EventBridge
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Amazon Kinesis Data Streams
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Amazon Managed Service for Apache Flink
Resources
