Haider Z.’s Post

Introducing Quadrafort’s Relational → MongoDB Migration #AI Tool In today’s data-driven world, migrating from traditional relational databases to MongoDB can unlock scalability, flexibility, and performance — but the process is often time-consuming and error-prone. That’s where Quadrafort’s AI Kitchen steps in. Our latest innovation — the Relational to MongoDB Migration AI Tool — automates schema transformation, data mapping, and validation using AI-assisted intelligence. ✅ Key Highlights: #Smart Schema Conversion: Automatically reinterprets relational models into optimal MongoDB document structures. #AI-driven Data Mapping: Detects relationships, normalizes inconsistencies, and preserves data integrity. #Validation & Optimization: Ensures consistency and efficiency across migrated collections. Zero Downtime Migration: Designed for seamless transition — even at scale. At AI Kitchen, we’re reimagining how data systems evolve — simplifying complex transitions with precision, automation, and intelligence. Watch the demo video below to see how our AI makes migration effortless. #TeamQuadrafort #AIKitchen #MongoDB #DataMigration #Innovation #ArtificialIntelligence #NoSQL #DataEngineering #TechTransformation

Announcing Tiger’s new Free Tier for Postgres 🚀 For years, we’ve powered some of the world’s most demanding Postgres workloads: multi-terabyte databases running industrial, SaaS, fintech, and devtool applications. But the world is changing. AI has made building experimental, agentic applications the new normal. So we turned our attention from scaling up to scaling down -- designing an architecture that’s efficient at full load, yet just as efficient at idleness. Technical deep dive post coming soon. This innovation now powers our new free tier for Postgres: lightweight, isolated, built for experimentation, and ready for growth. It's live today. Go explore, and let your agents cook. https://lnkd.in/eYwVgYiZ #Postgres #AI #Databases #AgenticPostgres Tiger Data (creators of TimescaleDB)

RelytONE：Everyone is a DBA RelytONE：All In One Postgres Relyt ONE feels like the future of Postgres. It ships everything — transactions, analytics, vector, full-text, graph,Time-Series,GIS — into a single, serverless engine. Unified, simple, free. Relyt ONE, the serverless, PostgreSQL-compatible database we've built from the ground up for this exact moment. I'm Philip, co-founder of DataCloud Tech, and over the past six months, we've watched hundreds of startups and AI teams ditch their fractured stacks—think Elasticsearch for search, DuckDB for analytics, Redis for caching—in favor of Relyt ONE. It's now powering over 200 million AI data queries daily, from RAG services in e-commerce chatbots to intelligent agents parsing multimodal feeds in healthcare diagnostics. In a world where MIT Sloan is calling out agentic AI as the inescapable trend for 2025, and vector databases are finally facing scrutiny for reliability issues in production RAG apps , Relyt ONE isn't just another tool—it's the unified engine that l https://lnkd.in/g_GKftva

RelytONE：Everyone is a DBA RelytONE：All In One Postgres Relyt ONE feels like the future of Postgres. It ships everything — transactions, analytics, vector, full-text, graph,Time-Series,GIS — into a single, serverless engine. Unified, simple, free. Relyt ONE, the serverless, PostgreSQL-compatible database we've built from the ground up for this exact moment. I'm Philip, co-founder of DataCloud Tech, and over the past six months, we've watched hundreds of startups and AI teams ditch their fractured stacks—think Elasticsearch for search, DuckDB for analytics, Redis for caching—in favor of Relyt ONE. It's now powering over 200 million AI data queries daily, from RAG services in e-commerce chatbots to intelligent agents parsing multimodal feeds in healthcare diagnostics. In a world where MIT Sloan is calling out agentic AI as the inescapable trend for 2025, and vector databases are finally facing scrutiny for reliability issues in production RAG apps , Relyt ONE isn't just another tool—it's the unified engine that l https://lnkd.in/g_GKftva

Yesterday evening, a friend called me in need. His PostgreSQL database was gone. Just ... vanished overnight. I opened Gemini and typed two sentences: "EC2 instance, PostgreSQL installed, worked fine yesterday, data missing this morning." 30 seconds later, I had the diagnosis AND the real solution: "Instance Store - ephemeral storage. Data wiped on restart. Best approach: Don't run your own database on EC2. Use Amazon RDS instead." Total time including validation: 90 seconds. Traditional debugging approach: 30+ minutes minimum. But here's the part nobody talks about, AI didn't solve this alone: 🔧 I knew enough to describe the problem correctly ⚡ I could validate if the diagnosis made sense 📊 I understood the architectural recommendations 🎯 I knew which parts to implement vs. question AI didn't replace expertise. It amplified it. The real power isn't AI doing your job. It's AI making you 20x faster at the job only you can do. This is why we integrate AI into operations at MHP. Not to replace engineers. To let them focus on problems that actually need human judgment. The questions that matter: - Can your team validate AI recommendations? - Do they know when to trust vs. question AI output? - Does your context get lost in generic AI answers? AI in operations is a multiplier. But only when paired with operational expertise. What's your experience using AI for technical troubleshooting? Wherever you go, make at least one thing better. ✨ #AI #TechOperations #DevOps #CustomSoftware #MHP

𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲 𝗼𝗻 𝗔𝘇𝘂𝗿𝗲 (𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲) 𝗜’𝘃𝗲 𝗯𝗲𝗲𝗻 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗮 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗳𝗼𝗿 𝗔𝗜 recommendations on Azure combining GPT embeddings, a vector DB, and classic ranking rules. Diagram attached. 𝗧𝗟;𝗗𝗥 Use GPT to embed users/items → store vectors → retrieve similar items fast → apply business rules → ship ranked recommendations through a low-latency API. 𝗛𝗢𝗪 𝗜𝗧 𝗪𝗢𝗥𝗞𝗦 (𝗘𝗡𝗗-𝗧𝗢-𝗘𝗡𝗗) Data sources: User actions & item metadata → Blob Storage + Cosmos DB Embeddings: GPT creates vectors for items & user context Serving: Deployed via Azure Machine Learning (Model Endpoint) API Gateway: Azure API Management for auth/routing/quotas Caching: Azure Cache for Redis for hot embeddings 6–8) Orchestration: Azure Functions for cache check + model calls Retrieval: Chroma vector DB k-NN similarity search Ranking: Re-rank with business rules (freshness, availability, margin, diversity) Delivery: Results returned via API to the user 𝗪𝗛𝗬 𝗧𝗛𝗜𝗦 𝗣𝗔𝗧𝗧𝗘𝗥𝗡 Low latency: Redis + Functions keep P95 tight Personalization: Embeddings capture behavior & content nuance Control: Rule-based re-rank aligns with business goals Composable: Swap vector DBs/models without breaking contracts 𝗧𝗘𝗖𝗛 𝗦𝗧𝗔𝗖𝗞 Azure ML • Azure OpenAI/GPT • Cosmos DB • Blob Storage • Azure Functions • Azure API Management • Azure Cache for Redis • Chroma (Vector DB) 𝗢𝗣𝗦 𝗧𝗜𝗣𝗦 Batch + on-demand embeddings (catalog vs. user context) Version your vectors (index per model/version; safe rollouts & A/B) Pipelines with AML for retraining & index refresh Guardrails & observability (APIM throttling, App Insights, vector recall dashboards) Curious how you’d adapt this for your catalog size and SLA targets. ♻️ Repost this to help someone who needs it. ➕ Follow Mohamad Alayoubi〽️ for more insights and resources. M⌁⥅ #Azure #GenerativeAI #RecommenderSystems #VectorSearch #MLOps #RAG #OpenAI #CosmosDB #AzureFunctions #APIManagement #Redis #Chroma

💡 What is Elasticsearch and Why It Matters in 2025 Ever wondered how apps like Netflix, Amazon, or LinkedIn deliver lightning-fast search results — even across millions of records? ⚡That magic is powered by Elasticsearch — an open-source, distributed search and analytics engine built on top of Apache Lucene. Instead of rows and tables, Elasticsearch stores JSON documents, making it perfect for: ✅ Full-text search (even with typos) ✅ Log and event analysis (ELK Stack) ✅ Real-time dashboards and monitoring ✅ AI + vector search (semantic understanding) It scales horizontally (add more nodes = more speed 🚀), supports fuzzy matching, and can analyze data in near real-time — all through simple REST APIs. Many modern systems use both: 🗃️ SQL → for transactions 🔍 Elasticsearch → for lightning-fast search and analytics In 2025, it’s no longer “just a search engine” — it’s the foundation for data-driven insights, observability, and AI-powered search. If you��ve never tried it, it’s worth exploring. It changes how you think about data. 💭 #Elasticsearch #SearchEngine #DataEngineering #BackendDevelopment #Scalability #SoftwareEngineering #BigData #Microservices #AI

We love seeing stories from the community about real-world migrations and lessons learned! 💡 Recently, Harsh Chandekar, Gen AI Consultant at EY - shared how their team migrated their application from MongoDB Atlas to Qdrant Vector Search, and why it turned out to be one of their most rewarding technical decisions of the year. Why migrate? - MongoDB Atlas offers vector search, but scaling can quickly become costly and inflexible for large-scale AI applications. - Qdrant, built from the ground up for AI-native workloads, provides an open-source alternative designed for speed, flexibility, and full control. Key improvements after migration: - ⬇️ 80–90% reduction in infrastructure cost compared to Atlas vector search. - ⚡ Up to 3× faster query performance on high-dimensional embeddings. - 📦 Zero vendor lock-in, thanks to Qdrant’s open and extensible architecture. 💡 Their migration journey covered: - Structuring payloads for hybrid sparse + dense retrieval. - Leveraging Qdrant’s advanced filtering and scoring capabilities for fine-tuned results. - Integrating Qdrant seamlessly into existing APIs, without rewriting the entire backend. This story perfectly captures why more teams are choosing Qdrant - for performance, transparency, and the freedom to scale AI search their way. Check out the full article: https://lnkd.in/gwjGBJD6 #Qdrant #VectorSearch #AI #OpenSource #HybridSearch #Migrations #SemanticSearch

Postgres for Agents: Fork Databases in 500ms and Let AI Agents Cook ... Safely 🎯 The first Postgres built for AI agents. Instant forks, advanced retrieval & agent training (a conversation with 🐯 Michael Freedman and 🐯 Ajay Kulkarni of Tiger Data (creators of TimescaleDB)) https://lnkd.in/gGvskkd8

Build an AI log analytics platform with Apache Doris + AWS Kinesis + Lambda + SageMaker + Amazon Bedrock. 👉👉 This solution can cut costs by 75–80%, giving you a modern AI stack with improved performance and observability. Parth Soni, a senior data engineer and AWS Solution Architect, shared this architecture at the recent Doris Summit 2025. The architecture looks like this: 1️⃣ AWS Kinesis ingests 5M logs/sec from applications, cloud infra, and security events. 2️⃣ Apache Doris stores the data, ingests millions/sec, providing inverted indexes, supporting sub-second full-text search at massive scale and lower infra costs. 3️⃣ AWS Lambda function extracts entities from AWS SageMaker and enriches each log with tags and other info. 4️⃣ Integrate with Amazon Bedrock to let users query logs in natural language (“Find all 500 errors in the checkout service yesterday”). Bedrock translates that request into SQL, and Apache Doris runs the query in seconds. 💸 The value of this architecture: - You get an AI-powered intelligent log analytics, with natural language search, auto-pattern discovery & predictive alerting. - About 80% lower costs vs. the old stack - 60% faster mean time to detection - 45% faster mean time to resolution - Happier on-call engineers spending less time hunting logs, more time fixing issues. 🤓 Parth summed it up: "It’s not just cost savings, it’s about smarter, faster, AI-driven operations. When your customers start asking, 'What else can they migrate to this architecture?' That's when you know you’ve built something special." See the full talk link in the first comment. #ApacheDoris, #AWS, #Sagemaker, #Bedrock, #Kinesis