Build highly performant data solutions with serverless analytics (ANT335)
Key Takeaways
Performance Optimization for Data Systems
The Performance Pentagon: A mental model for balancing 5 critical factors - latency, scalability, efficiency, reliability, and cost.
Purpose-built Services: Choosing the right service for different query patterns and workload characteristics (e.g., Amazon Redshift for complex queries, Amazon Athena for interactive queries).
Open Table Formats: Using formats like Apache Iceberg to optimize read queries, enable atomic transactions, and integrate with metadata stores like AWS Glue.
Automated Optimizations: Leveraging features like automated materialized views, table optimizations, and join reordering in services like Amazon Redshift Serverless and Amazon Athena.
Scaling with AI-driven Capabilities: Using AI-driven scaling in Amazon Redshift Serverless and provisioned capacity in Amazon Athena to handle variable workloads.
Real-time Intelligence Use Case
Serverless Event-driven Streaming Pipeline: Using services like AWS Lambda, Amazon MSK, and Amazon Managed Streaming for Apache Flink to build a real-time intelligence system.
Integrations with Open Search and AWS Glue: Leveraging features like zero-ETL ingestion, smart caching, and auto-scaling in Open Search, and job run queues and usage profiles in AWS Glue.
Self-service Analytics Use Case
Data API Service: Using a serverless architecture with AWS Cognito, API Gateway, and Lambda to provide on-demand data access to customers.
Embedded Analytics with Amazon QuickSight: Embedding QuickSight dashboards and authoring experience within the product, leveraging namespaces and row-level security for multi-tenancy.
Continuous Improvement Approach
Serverless-first Strategy: Evaluating and selecting the right serverless services for each component of the data architecture.
Ongoing Reviews: Utilizing AWS Trusted Advisor and AWS Well-Architected Framework to review and optimize performance continuously.
Trade-off Considerations: Balancing the performance pentagon factors to make informed decisions about caching, compression, and SLAs.
Monitoring and Baselining: Establishing performance baselines and continuously optimizing through monitoring and observability.
Next Steps
Data Strategy Alignment: Engage with AWS for a data-driven strategy alignment and pilot program.
Modernization and Migration: Leverage the AWS Reimagine Data program for migration and modernization of on-premises data systems.
AWS Well-Architected Reviews: Conduct regular reviews of your data architecture using the AWS Well-Architected Framework.
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