AWS Databases & Analytics

🚀 Simplify log analytics & accelerate incident investigations with new Agentic AI capabilities. 👉 https://go.aws/3QBGyN4 Amazon OpenSearch Service just launched #AgenticAI features to transform how you investigate incidents—at no additional cost. Stop wrestling with complex query syntax & manual log analysis. Here's what's now available: 💬 Agentic Chat: Context-aware agent that understands natural language, performs agentic reasoning & leverages multimodal understanding of visualizations to analyze data directly in your UI 🔍 Investigation Agent: Autonomously plans investigations, executes queries & reflects on results to generate ranked root cause hypotheses with full transparency into reasoning 🧠 Agent Memory: Maintains context across sessions for seamless conversation continuity Built for #Observability teams using #OpenSource tools to reduce MTTR & improve incident response.

⚡ Build your first GraphRAG-powered agent in under an hour — no graph expertise needed. Watch the full training. 👉 https://go.aws/4vNX6BA Standard #RAG retrieves semantically similar content. But what about relevant information that doesn't look similar at all? That's where #GraphRAG changes the game. In this Databases for AI livestream, AWS experts walk you through building a GraphRAG agent from scratch using the open source GraphRAG Toolkit & #AmazonNeptune Analytics: 🔹 Your AI keeps giving incomplete answers — because vector search alone misses context buried in other documents 🔹 You want to go from raw documents to a working GraphRAG app, but don't know where to start — it takes just a few lines of Python 🔹 You need your agent to reason across relationships, not just keywords — MCP makes it easy to plug GraphRAG into any agent framework The result? An agent that doesn't just find what's similar — it finds what actually matters. Hit play on the teaser & then dive into the full build. ⬆️

🔍 See how Ring built semantic video search at billion-scale with Amazon RDS for PostgreSQL & pgvector. 👉 https://go.aws/4mJsXPE Ring stores 100-200 billion embeddings across 9 AWS Regions, serving millions of customers with natural-language video search like "dog in my backyard" or "package delivery"—all in under 2 seconds. Key architectural decisions: 🎯 User-based table partitioning for query performance & data isolation ⚡ Parallel search with 100% recall (no ANN indexes) 💾 EBS-optimized instances with aggressive parallelism (16 workers) 🔥 pg_prewarm for cold-start optimization The counterintuitive choice? Removing vector indexes entirely & relying on parallel sequential scans, which delivered perfect recall while still meeting their sub-2-second latency target. #AmazonRDS #PostgreSQL #VectorSearch

🚀 Amazon Aurora serverless now delivers up to 30% better performance & smarter scaling for your most demanding workloads. 👉 https://go.aws/4e1FX0N Built for variable workloads such as agentic AI workloads, which typically have bursts of activity, long idle windows, & unpredictable concurrency. #AmazonAurora #Serverless scales with your agents, not against them. No manual intervention required. It scales to zero when idle, so you pay nothing when your database isn't in use. Customers can now achieve: ⚡ Up to 30% better performance, making serverless ready for even more demanding use cases 🧠 Enhanced scaling algorithm that is aware of your workload pattern Start building with Aurora today!

Welcome back to Data Streaming Now! In this episode, Ali Alemi (Principal Data Streaming Architect at AWS) and Mindy Ferguson (VP of Technology at AWS) tackle one of the most persistent challenges facing teams running Apache Kafka in production: operating distributed streaming systems reliably in cloud environments. Running Kafka in the cloud sounds simple in theory—but in practice, cloud constraints create operational complexity that can make your streaming infrastructure unstable. Network throughput quotas limit how fast you can replicate data. Storage throughput caps restrict your write performance. Individual nodes fail unexpectedly. Entire Availability Zones can go down. And when any of these happen, traditional Kafka architectures force you into expensive, time-consuming data rebalancing operations that work against the elastic promise of cloud computing. The result? Operational burden that prevents teams from scaling at the speed their business demands. We'll dive deep into how Amazon MSK Express Brokers fundamentally solve these operational challenges by decoupling storage from compute, eliminating data rebalancing overhead, and providing true cloud-native elasticity for Kafka workloads—allowing you to scale seamlessly, recover from failures instantly, and operate with confidence even under the most demanding conditions. Join us live as we explore Amazon MSK Express Brokers, demonstrate resilience under failure conditions, and—yes—prove it works with real systems and real metrics. No theory. No slides. Just production-grade solutions to Kafka's toughest operational challenges.

💡 Your standby Regions shouldn't cost the same as your primary. Now they don't have to. 👉 https://go.aws/47WpdUP Amazon DocumentDB 5.0 global clusters now support serverless instances. Your secondary Regions can run at minimal capacity during normal operations & auto-scale only when traffic demands—or when a failover occurs. #AmazonDocumentDB What this changes for multi-Region builders: 🔹 Up to 10 secondary Regions, each scaling independently via DocumentDB Capacity Units (DCUs) 🔹 Failover promotion to full read/write capability in under one minute 🔹 No more pre-provisioning instances sized for worst-case scenarios across every Region This is especially relevant for #Serverless multi-Region architectures — SaaS platforms, multi-tenant workloads & apps with time-zone-driven usage peaks. Pair with AWS Lambda & Amazon API Gateway for a fully serverless stack. Honest tradeoff: If your secondary Regions handle consistent heavy read traffic, provisioned instances may still be more cost-effective. Serverless shines brightest when standbys are mostly idle. #Database Check out the demo walkthrough below. 👇

🧊 Whether you're a seasoned Apache Iceberg practitioner or just beginning your journey, join AWS experts at Iceberg Summit 2026. 👉 https://go.aws/4d4TYuj AWS sessions cover: 🎯 Fine-Grained Metadata Commits in Apache Iceberg: Improve concurrency & reduce commit conflicts in high-throughput environments 💠 A Rusty Future: Bringing Iceberg to the Rust Data Ecosystem: Integrate Iceberg tables with Rust-based data processing pipelines 📊 The Evolution of Semi-Structured Data: Moving from JSON Strings to Iceberg V3 Variants: Query nested data structures without schema flattening ⚡ Performance Tuning for Streaming Ingestion into Apache Iceberg: Reduce small file overhead & optimize compaction strategies for real-time workloads Register to attend sessions on Iceberg architecture & performance. #AWSAnalytics #Developer #AWSatApacheIcebergSummit

Amazon Kinesis Data Streams On-demand Advantage mode delivers 60%+ cost savings. Read the blog to learn more. 👉 https://go.aws/4dzttgD In this post, we walk through three real-world scenarios showing how On-demand Advantage reduces costs compared to On-demand Standard without sacrificing performance or flexibility. Reduce costs across consistent high-throughput workloads, extended data retention, & architectures with multiple Enhanced Fan-Out consumers — with savings ranging from 41% to 67% depending on the use case. On-demand Advantage is a good fit if your streaming workloads run consistently at scale, use multiple consumers, or need longer data retention. #AWS #DataStream #BigData

⚡Amazon Redshift increases performance of new queries by up to 7x. Try it today! 👉 https://go.aws/4lylT7Y Get faster query results from the first run. #AmazonRedshift now delivers accelerated query startup times for low-latency #SQLAnalytics workloads like near real-time analytics, BI dashboards, & agentic AI. Amazon Redshift has optimized its code generation engine, so new queries start faster and deliver performance consistent with subsequent runs. #CloudDataWarehouse

🎮 Read Yggdrasil Gaming’s journey from BigQuery to AWS that reduced their data processing costs by 60%. 👉 https://go.aws/47wMoVy Yggdrassil Gaming built an Apache Iceberg-based modern lakehouse on Amazon S3, Amazon Athena, & AWS Glue Data Catalog to solve advanced analytics & AI/ML use cases such as player behavior modeling, predictive game recommendations, & fraud detection. No more dual-cloud complexity. No more proportional cost scaling. Just one open, unified data platform, queryable across Athena, Spark, & dbt. The result: 60% reduction in data processing costs, 75% lower latency for analytics results. #ApacheIceberg #DataArchitecture #CloudMigration