Category: Intermediate (200)

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.

Dutchie, a leading technology platform serving the cannabis industry, manages critical operations for thousands of dispensaries across multiple states, processing millions of transactions annually. In this post, we explore how Dutchie successfully navigated the challenges of migrating mission-critical workloads to Amazon RDS for SQL Server in preparation for 4/20 week in 2025.

Amazon RDS instances running on legacy file systems face several limitations. Storage is capped at 16 TiB, and some engines, including MySQL, MariaDB, and PostgreSQL, may encounter per-file size limits of approximately 2 TiB due to file system constraints, even though the database engines themselves support larger objects. In this post, I show you how to upgrade an RDS instance to the current file system with minimal downtime using Amazon RDS blue/green deployments.

In this post, you learn how Amazon Aurora now provides encryption at rest by default for all new database clusters using AWS owned keys. You’ll see how to verify encryption status using the new StorageEncryptionType field, understand the impact on new and existing clusters, and explore migration options for unencrypted databases.

When running database migration with AWS DMS, you may encounter opportunities to streamline your workflow: interpreting error messages, understanding configuration parameter relationships, and navigating between the console, documentation, and community forums during troubleshooting. What if you could have an AI-powered assistant that understands your migration context, diagnoses issues in real-time, and provides actionable guidance—all within your workflow? In this post, we show you have Amazon Q integration with AWS DMS can transform your database migration experience.