Category: Intermediate (200)

In this post, we show how the integration between Vercel and AWS Databases solves this and invite you to participate in the H0 hackathon.

Today, we are excited to announce the general availability of Amazon Aurora MySQL 8.4, our latest major version, compatible with community MySQL 8.4.7. This release marks an important milestone for Aurora MySQL customers, introducing a simplified versioning model aligned directly with community MySQL, along with a streamlined patch version experience, and the full set of community MySQL 8.4 enhancements. In this post, we discuss the customer challenges that this release addresses, introduce Aurora MySQL 8.4, walk through the new versioning approach and its benefits for customers, cover the key capabilities delivered in Aurora MySQL 8.4, and show you how to get started.

Today, we are announcing ExtendDB, an open source Amazon DynamoDB-compatible adapter with pluggable storage backends, released under the Apache 2.0 License. ExtendDB implements the DynamoDB wire protocol and ships with PostgreSQL as its first backend, so any AWS SDK, CLI, or tool that works with DynamoDB works with ExtendDB unchanged. In this post, we introduce ExtendDB, walk through getting started, and explain the architecture. This is a v0.1 release for development, testing, and experimentation.

In this post we demonstrate how Nine Entertainment achieved a 98% cost reduction by migrating to Amazon ElastiCache Serverless for Valkey while improving scalability and eliminating manual intervention during peak events.

Amazon ElastiCache now supports Valkey 9.0. This brings the latest community-driven innovations from the Valkey open source project to address the performance and capability requirements of applications as they grow more data-intensive and latency-sensitive, such as real-time analytics, AI-driven retrieval, and high-throughput caching. In this post, we explore how these enhancements help customers build faster applications, streamline architectures, and support new real-time and AI-driven workloads.

New search capabilities are available in ElastiCache version 9.0 for Valkey. In this post, we walk through the new search capabilities, show how they work together, and build a search and recommendation engine from scratch.

This post demonstrates how in our testing upgrading to Graviton4-based R8g instances on Amazon DocumentDB (with MongoDB compatibility) version 5.0 and 8.0 delivers up to 63% better performance compared to Graviton2-based R6g instances on the Sysbench benchmark. This improvement comes at only a 5% cost increase.

In this post, we share Ring’s billion-scale semantic video search on Amazon RDS for PostgreSQL with pgvector architectural decisions vs alternatives, cost-performance-scale challenges, key lessons, and future directions. The Ring team designed for global scale their vector search architecture to support millions of customers with vector embeddings, the key technology for numerical representations of visual content generated by an AI model. By converting video frames into vectors-arrays of numbers that capture what’s happening (visual content) in each frame – Ring can store these representations in a database and search them using similarity search. When you type “package delivery,” the system converts that text into a vector and finds the video frames whose vectors are most similar-delivering relevant results in under 2 seconds.

In this post, we provide a guide to migrating from Redis OSS to ElastiCache for Valkey, incorporating different migration strategies and AWS best practices. Additionally, we highlight a customer’s successful migration to Valkey, which maintained their robust performance standards while achieving a 20% reduction in ElastiCache cluster costs.

You can use the AWS Database Migration Service Schema Conversion (AWS DMS SC) with generative AI feature to accelerate your database migration to AWS. This feature automatically handles the conversion of many database objects during migration by using traditional rule-based techniques and deterministic AI techniques. In this post, we demonstrate how Amazon Q Developer delivers generic solutions for complex AWS DMS SC issues, intelligently converts database stored procedure code from source to target database-compatible code, and automatically generates comprehensive test cases to validate your migrated database objects.