Optimizations – A Tech Ref

S3 Performance considerations

Amazon S3 (Simple Storage Service) is a highly scalable and durable object storage service offered by AWS. It provides a secure and cost-effective solution for storing and retrieving large volumes of data. To optimize S3 performance, several key optimizations can be implemented.

These include distributing objects across multiple buckets or prefixes within a bucket to avoid bottlenecks, utilizing multi-part upload for large objects to improve upload efficiency, leveraging S3 Transfer Acceleration for faster data transfers over long distances, implementing caching and content delivery through services like Amazon CloudFront, optimizing request patterns by parallelizing read and write operations, monitoring performance metrics using Amazon CloudWatch, and considering the appropriate S3 storage class based on data access patterns and cost requirements.

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DynamoDB and DAX

DAX (DynamoDB Accelerator) is a fully managed, in-memory caching service for DynamoDB that can significantly improve read performance by reducing the number of requests to DynamoDB. DAX is designed to work with DynamoDB, and it provides a fast, scalable, and highly available caching layer that sits between your application and DynamoDB.

DAX can significantly improve read performance by reducing the number of requests to DynamoDB and caching frequently accessed data in memory. By using DAX, you can improve the performance and cost-effectiveness of your application while minimizing infrastructure management.

DynamoDB Performance and Optimizations

DynamoDB is designed to provide high performance and scalability, even at scale. It is a highly available and durable NoSQL database service that can handle large volumes of data and requests with low latency.

DynamoDB is a distributed database service that uses sharding to partition data across multiple servers called nodes. Each node is responsible for storing a subset of the data, and the partition key is used to determine which node stores the data. This allows DynamoDB to scale horizontally by adding more nodes to the cluster as the data and request volume grows.