# ADVOPS04-BP01 Implement operational procedures based on data classification and latency requirements
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 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
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 For low-latency data (bid data, user profiles, real-time impressions): 
+  Implement multi-AZ deployments with automatic failover mechanisms to facilitate continuous availability 
+  Configure monitoring with short evaluation periods appropriate for detecting real-time issues 
+  Establish dedicated rapid-response procedures for critical alerts affecting bidding operations 
+  Implement circuit breakers in API calls to help block cascading failures during service degradation 
+  Create runbooks for emergency traffic management during extreme load conditions 
+  Configure auto-scaling with aggressive scaling policies to handle sudden traffic spikes 
+  Implement local caching strategies to reduce database load for frequently accessed data 
+  Set up dedicated dashboards with high-frequency metric collection for real-time monitoring 

 For medium-latency data (behavioral data, campaign optimization): 
+  Configure batch processing jobs with appropriate completion targets for campaign optimization 
+  Implement queue management with automated retry mechanisms for failed operations 
+  Set up monitoring with balanced evaluation periods suitable for near real-time operations 
+  Create standard incident response procedures with appropriate escalation paths 
+  Implement data validation checks with error handling for data quality issues 
+  Configure auto-scaling based on processing queue depth and scheduled campaign activities 
+  Set up dashboards with appropriate refresh rates for campaign management operations 

 For high-latency data (historical data, analytics): 
+  Schedule batch processing during off-peak hours to minimize impact on real-time operations 
+  Implement cost-optimized storage strategies with appropriate data lifecycle policies 
+  Configure monitoring with periodic health checks and summary reporting 
+  Create standard support procedures with appropriate response times for non-critical systems 
+  Implement automated data quality validation with notification mechanisms 
+  Configure resource allocation with scheduled scaling based on known processing windows 
+  Establish regular performance review processes with trend analysis 

 For specialized advertising data types: 
+  Fraud detection data: Implement optimized processing pipelines with appropriate monitoring and escalation procedures designed for the critical nature of fraud detection 
+  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 
+  AWS Systems Manager 
+  Amazon EventBridge 
+  Amazon Kinesis Data Streams 
+  Amazon Managed Service for Apache Flink 

## Resources
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+  [AWS for Advertising & Marketing](https://aws.amazon.com/advertising-marketing/adtech-real-time-bidding/) 
+  [Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1](https://aws.amazon.com/blogs/big-data/architectural-patterns-for-real-time-analytics-using-amazon-kinesis-data-streams-part-1/) 
+  [Streaming architecture patterns using a modern data architecture](https://docs.aws.amazon.com/whitepapers/latest/build-modern-data-streaming-analytics-architectures/streaming-analytics-architecture-patterns-using-a-modern-data-architecture.html)