Anirudh Garg

People often ask me about the differences in architecture between Amazon Dynamo (the 2007 SOSP paper), DynamoDB (the AWS serverless NoSQL database), and Aurora DSQL (the AWS serverless SQL databases). I captured the key points on durability, high availability, programming model, and trade-offs in my blog: https://lnkd.in/gkMG_tVD TL;DR: Despite the shared name, Dynamo and DynamoDB have little in common architecturally, and make very different trade-off decisions.

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I am always excited to hear about customers's journeys on how they use Google Cloud Databases innovations to transform the experience of their users. With state-of-the-art AI models, Agentic AI (MCP), AI retrieval, AI Query Engine, AlloyDB, a 100% PostgreSQL database, is becoming the platform for customers to re-engineer their applications and systems for AI. In this blog by Vishal Vaibhav and Melissa Ludack, Target shares how they partnered with Google, leveraging AlloyDB's industry-leading AI capabilities to reimagine their search architecture to bring structured data (SQL) & unstructured data (text, vector search) to overhaul their search bar! Target has a sophisticated system for search, with AI models that enable nuanced semantic matching using AlloyDB's ScaNN index technology. It is a great testament of the research at Google in AI (ScaNN is the underlying tech used by Google Search and YouTube, for example) to see it also powering retail search on Target.com and helping their users! Congratulations to Target and look forward to partnering with you to bring further exciting advances in AI in GCP Databases to delight your customers! #Partnership #Databases #RetailInnovation #TargetSearch #GoogleCloud Lots of great insight in their blog on how Target leverages AlloyDB AI to create personalized, delightful shopping moments for their customers every day. https://lnkd.in/gaGPNZD5

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AWS transform was featured in the Amazon Q2 quarterly earnings release as "General availability of AWS Transform, an AWS agent that cuts mainframe modernization timelines from years to months, completes VMware network conversions up to 80x faster, and reduces operating costs for .NET applications by up to 40%." See https://lnkd.in/gSrsSC72.

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This is a great example of the real-world impact of AI capabilities within AlloyDB for PostgreSQL ! Target set out to modernize its digital search experience to better match guest expectations and support more intuitive discovery across millions of products. To meet that challenge, they rebuilt their platform with hybrid search powered by filtered vector queries and AlloyDB AI. The result: a faster, smarter, more resilient search experience that’s already improved product discovery relevance by 20% and delivered measurable gains in performance and guest satisfaction. #alloydb #postgresql #gcp

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GPT-5 has arrived, and it's live across M365 Copilot, GitHub Copilot, Copilot, and Azure AI Foundry! It's the most powerful new LLM across several benchmarks and everything runs on Azure, with Foundry providing the coordination and security to support enterprise use. Azure AI Foundry's endpoint using model router allows developers to automatically direct their prompts to the most suitable version — whether it’s GPT-5, GPT-5-mini, GPT-5-nano, or GPT-5-chat. https://aka.ms/GPT-5-blog

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One of the hardest problems with building AI Agents has been knowing if your knowledge configuration is truly set up for success. With #AzureAIFoundry and #AzureAISearch, we now provide a systematic framework to evaluate retrieval quality, grounding, and synthesis across different setups. This gives developers and enterprises a clear way to compare configurations, tune parameters, and deliver more accurate, reliable answers. The result: a repeatable way to optimize your RAG workflows—turning agent building from trial and error into a disciplined process. Chang Liu walks through the framework in detail here: https://lnkd.in/eNPpQzhr

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