SCPERF03-BP01 Select your database architecture based on workload
Purpose-built data storage for your workloads can help increase the performance efficiency of the overall system, as well to be more resilient in case of failures.
Desired outcome: Purpose-built data storage. Increased performance efficiency of the overall system.
Benefits of establishing this best practice: Scalability, resilience, and end user performance improvement.
Level of risk exposed if this best practice is not established: High.
Implementation guidance
Select database options that align with your performance requirements, using different database technologies for different purposes, such as Amazon Timestream time-series database for storing ticking market data, rather than a one-size-fits-all use of traditional relational databases. Also, Amazon RDS is a straightforward relational database service optimized for total cost of ownership. It is simple to set up, operate, and scale with demand. Amazon RDS automates the undifferentiated database management tasks, such as provisioning, configuring, backups, and patching.
Implementation steps
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Analyze supply chain data access patterns and performance requirements to determine optimal database architectures.
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Implement purpose-built databases for specific use cases, such as time-series databases for IoT sensor data and document databases for product catalogs.
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Configure Amazon RDS for transactional supply chain data that requires ACID compliance and complex queries.
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Deploy NoSQL databases like Amazon DynamoDB for high-throughput, low-latency applications such as inventory tracking.
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Establish database performance monitoring and optimization processes to maintain continued efficiency as data volumes grow.
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Implement automated backup and disaster recovery strategies to maintain data availability and business continuity.
