Great insights from MongoDB on how Rox is leveraging AI agents to revolutionize revenue cycle management! The migration from DynamoDB to MongoDB showcases the power of choosing the right database for enterprise-scale AI workloads. This is exactly the kind of digital transformation driving innovation across industries. #MongoDB #AI #DatabaseMigration #CloudComputing #DigitalTransformation #EnterpriseAI #TechInnovation #DataManagement #RevenueOperations #Trending https://lnkd.in/enyqudEU ENCS | LinkedIn

Amazing case study from @MongoDB! Rox's migration from DynamoDB shows the power of choosing the right database architecture for AI agents at enterprise scale. The future of revenue operations is being built on robust, scalable infrastructure. https://lnkd.in/enyqudEU https://lnkd.in/e5F_kdZP #MongoDB #AIAgents #DatabaseMigration #EnterpriseScale #TechInnovation #CloudInfrastructure #DataArchitecture #RevenueOperations #Trending #Viral #AI #MachineLearning

Last week Clarity Business Solutions, Inc was honored to sponsor the MongoDB Public Sector Summit at the International Spy Museum in Washington DC. We met with leadership across DoW, Health, and Intelligence agencies to learn about their latest mission modernization challenges. Federal AI and data platform modernization efforts are leaning into commercial strategies and product based acceleration while consistently focusing on data quality, trust, and compliance. Clarity shared how we deliver secure MongoDB solutions to the mission through our certified ProServe engineers, OMaDA installation accelerator, and SPARK-AI framework! Check out more information at the links below: Clarity's MongoDB Professional Services: https://lnkd.in/eyd3H6jF OMaDA installation accelerator - https://lnkd.in/e5-JsWh3 #MongoDB #2026DCSummit #ProServe #OMaDA #Claritybizsol

Lately, I was working on building systems using Amazon Web Services (AWS) services like Lambda, OpenSearch, DynamoDB, and MSK, and it completely changed how I think about backend architecture. Here’s a simple way I now look at it: • AWS Lambda – Run code without worrying about servers • Amazon OpenSearch – Power fast search and analytics • DynamoDB – Handle scalable, low-latency data storage • MSK (Managed Streaming for Kafka) – Stream and process real-time data What really clicked for me is how these services connect together: ➡️ Events flow through MSK (Kafka) ➡️ Lambda processes those events ➡️ Data gets stored in DynamoDB ➡️ And becomes searchable via OpenSearch This ecosystem shows how powerful event-driven architecture can be - scalable, decoupled, and built for real-time applications. Still early in my learning journey, but working on these technologies is helping me think beyond just writing code, and more about designing systems. Excited to go deeper 🚀 #AWS #Serverless #CloudArchitecture #DynamoDB #OpenSearch #Kafka #MSK #LearningInPu

[Tech Community] GraphRAG and PostgreSQL integration in docker with Cypher query and AI agents (Version 2*). This is update from previous blog (version 1): GraphRAG and PostgreSQL integration in docker with Cypher query and AI agents | Microsoft Community... #azure #techcommunity https://lnkd.in/erc-N3DH

Something quietly changed in the RAG game at AWS and most people missed it. AWS Bedrock now lets you point a Knowledge Base directly at S3. No vector database. No embedding pipelines. No Pinecone. No infrastructure overhead. Drop your documents in S3. Bedrock handles chunking, embedding, indexing, retrieval. All managed. All serverless. The traditional RAG stack has 6-7 moving parts. S3 Knowledge Base collapses it to two: your documents and Bedrock. We were initially building with OpenSearch Serverless as our vector store. It works, but the cost adds up fast. OpenSearch Serverless charges for OCUs (OpenSearch Compute Units) that run continuously, minimum 2 OCUs for indexing + 2 for search. That is roughly $700-1000/month just to keep the vector store alive, before you even factor in embedding compute and data ingestion pipelines. For a POC or early stage product, that is hard to justify. S3 Knowledge Base flips this completely. Storage cost is pennies. You pay per query, not per hour. For our use case with a few hundred documents and moderate query volume, we are looking at 80-90% cost reduction compared to the OpenSearch setup. And zero infra management. We have been exploring this for an inbound conversational AI agent for one of our customers. Still in POC and testing, but early results are promising. Retrieval quality is solid, latency is acceptable, and the operational simplicity is a massive win. No vector DB to babysit. Just update docs in S3 and the knowledge base stays current. The interesting shift: the bottleneck moves from "how do we build RAG infrastructure" to "how do we structure our knowledge well." Which is where it should have been all along. If you are building RAG on AWS and still running OpenSearch or a managed vector DB for small to mid-size knowledge bases, take a serious look at this. #AWS #AWSBedrock #GenAI #RAG #KnowledgeBase #OpenSearch #ConversationalAI #Serverless #AI Sagar Thakare Swami Sivasubramanian Banjo Obayomi Antje Barth Adarsh Pandey Aravind M

This Thursday, Henrique Leandro Lea will go live with a fast-paced walkthrough of: - The top reasons teams outgrow Amazon Aurora - Architectural constraints that hurt performance and availability at scale - How distributed SQL with TiDB lets you escape lock-in and design for growth If you own database or platform decisions, now is the time to join. 🔗 Last chance to register for “Escape from Amazon Aurora: How to Unlock True Database Freedom and Performance”: https://ow.ly/jvi150Yylag If you're not convinced ask yourself if you feel any of these constraints!! 1️⃣ Cost creep that sneaks up on you. What starts simple can become unpredictable fast. 2️⃣ Performance that isn’t always predictable A Database can be fast, until it isn’t. Teams often run into latency spikes or inconsistent replica performance under load, which makes tuning and scaling harder than expected. Plus there is the dreaded downtime ;) 3️⃣ Scaling limits on writes. Reads scale nicely with replicas… But writes? Still bottlenecked by a single primary node. For high-growth or write-heavy workloads, this becomes a real constraint. Need to be agile. Now layer in where things are going with AI👇 Big push toward AI agents and agentic workloads need a scalable and cost effective system that can handle: - Highly concurrent - Spiky and unpredictable - Write-heavy (memory, state, logs, embeddings) - Always-on and globally distributed - Needs to be reliable Aurora and others can still be a strong choice for many workloads. But if you’re building for the next wave of AI-native, agent-driven systems you have to ask: Is your database designed for the future… or the past? Can your current database handle... - Massive parallel writes from thousands of agents - Low-latency, globally distributed state. - Elastic scaling that keeps up with unpredictable AI-driven traffic How are you thinking about your databases in the age of AI? #TiDB #AIDatabase #AIAgents #TiDBCloud #DistributedSQL #MySQL #CloudNative

Serverless compute scales instantly. Your database writes do not. Most serverless backends hit a wall long before Lambda does. The bottleneck is not the function, it is the single write endpoint behind it. You can spin up a thousand concurrent invocations, but if they all queue at one database connection, elasticity is an illusion. Aurora Limitless changes that calculation. It distributes write transactions across multiple instances while keeping ACID guarantees intact. AWS published a benchmark showing 2.55 million commits per second at 2.4 milliseconds average latency. That is not a theoretical ceiling, that is a demonstrated floor. The implication for performance testing: your load profiles now need to reflect what the database can actually handle, not what you assumed it could not. Write-heavy scenarios that once required DynamoDB or manual sharding can now stay relational without hitting contention. But serverless still is not self-tuning. Connection storms, cold starts, and ACU scaling curves all require deliberate validation under realistic load. The architecture got better. The need for rigorous testing did not go away. #PerformanceTesting #ServerlessArchitecture #DatabaseScaling

You can spin up a thousand concurrent invocations, but if they all queue at one database connection, elasticity is an illusion....

We almost picked Aurora because it scales better. That would've been a $400/month mistake. At ZoeWhy™, our database handles AI-scored leads, outreach logs, ICP data. Standard SaaS stuff. The obvious move? Aurora. Auto-scaling storage. 15 read replicas. Sub-10ms replica lag. Sounds perfect on paper. But we're bootstrapped. Our actual read volume? Moderate. Our write patterns? Predictable. Aurora costs aprox 20% more than RDS PostgreSQL. For our stage, that's real money compounding monthly , for headroom we don't need yet. So we went RDS Multi-AZ with one read replica. 1. Automated failover. 2. Zero-downtime AZ switching. 3. Read workload offloaded. 4. Total cost: a fraction of what Aurora would've been. The principle: don't architect for the traffic you want. Architect for the traffic you have , with a clear migration path for when you outgrow it. Aurora's brilliant. Just not at every stage. At Inceptory Technology Private Limited as a offshore tech partner of many startup, we constantly see this , clients prefer Aurora serverless before even onboarding 10 customers. We advise them get the traction first then then upgrade to Aurora if scale demands it. Are you paying for infrastructure you're not using yet? #aws #cloudarchitecture #systemdesign